diff --git a/.all-contributorsrc b/.all-contributorsrc
index 8d6f60fa8..e05333be5 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -184,6 +184,16 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "meraldoantonio",
+      "name": "Meraldo Antonio",
+      "avatar_url": "https://avatars.githubusercontent.com/u/37468543?v=4",
+      "profile": "https://github.com/meraldoantonio",
+      "contributions": [
+        "code",
+        "example"
+      ]
     }
   ]
 }
diff --git a/examples/04_bayesian_conjugate.ipynb b/examples/04_bayesian_conjugate.ipynb
new file mode 100644
index 000000000..3e21a9574
--- /dev/null
+++ b/examples/04_bayesian_conjugate.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bayesian Conjugate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from skpro.distributions import Beta\n",
+    "from skpro.regression.bayesian_proportion import BayesianProportionEstimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conjugate Bayesian analysis is a statistical method where the posterior distribution belongs to the same family as the prior distribution. \n",
+    "\n",
+    "In this notebook, we will use the `skpro` estimator `BayesianProportionEstimator` to demonstrate the application of Bayesian conjugates in the estimation of proportion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the data above, we aim to estimate the proportion $p$ of heads. However, due to the small sample size, we might be uncertain about our estimate. This is where Bayesian analysis, and specifically the use of conjugate priors, becomes valuable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Motivation for Estimating Proportions: The Coin Toss Example\n",
+    "\n",
+    "Estimating proportions is a fundamental problem in statistics, often arising in situations where we want to understand the likelihood of a specific outcome. A classic example is the coin toss.\n",
+    "\n",
+    "Imagine we have a coin, and we want to determine if it is fair. A fair coin has an equal probability (0.5) of landing heads or tails. To estimate this probability, we can perform a series of coin tosses and observe the outcomes.\n",
+    "\n",
+    "Suppose we toss the coin 10 times and observe the following results:\n",
+    "\n",
+    "- Heads: 8 times\n",
+    "- Tails: 2 times\n",
+    "\n",
+    "This coin toss sample is represented in the pandas Series `y` shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    1\n",
+       "2    1\n",
+       "3    1\n",
+       "4    1\n",
+       "5    1\n",
+       "6    1\n",
+       "7    0\n",
+       "8    0\n",
+       "9    1\n",
+       "Name: head, dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the number of ones and zeros\n",
+    "ones = [1] * 8\n",
+    "zeros = [0] * 2\n",
+    "\n",
+    "# Combine them into a single list\n",
+    "data = ones + zeros\n",
+    "\n",
+    "# Shuffle the list to randomize the order\n",
+    "np.random.shuffle(data)\n",
+    "\n",
+    "# Create a Series from the shuffled list\n",
+    "y = pd.Series(data, name=\"head\")\n",
+    "\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on this data, we aim to estimate the proportion `p` of heads. However, due to the small sample size, we might be uncertain about our estimate. This is where Bayesian analysis becomes valuable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Prior\n",
+    "\n",
+    "A Bayesian analysis begins with a **prior belief** about the parameter of interest, which in this case is the proportion `p`. This prior belief is represented by a **prior _distribution_**, which encapsulates our initial assumptions about `p` before observing any data. The choice of distribution can be based on previous knowledge, expert opinion, or even non-informative (if we have no prior knowledge).\n",
+    "\n",
+    "A **conjugate prior** is a specific type of prior distribution that, when combined with the likelihood of the observed data, yields a posterior distribution that belongs to the same family as the prior distribution. This property simplifies the process of updating our beliefs with new data, as the mathematical form of the distribution remains consistent.\n",
+    "\n",
+    "The **Beta distribution** is often chosen as the prior distribution because it is a conjugate prior to the Binomial likelihood, which describes the probability of observing a given number of heads in a series of coin tosses.\n",
+    "\n",
+    "\n",
+    "The distribution, denoted as $\\text{Beta}(\\alpha, \\beta)$, is parameterized by two positive parameters, $\\alpha$ and $\\beta$. These parameters reflect our prior beliefs about the proportion $p$. For example:\n",
+    "\n",
+    "- If $\\alpha = 1$ and $\\beta = 1$, we have a uniform prior, which indicates that all values of $p$ between 0 and 1 are equally likely.\n",
+    "- If $\\alpha > 1$ and $\\beta > 1$, the prior might be more peaked around a particular value, indicating a stronger prior belief about $p$.\n",
+    "\n",
+    "Suppose in our case that we have some conviction that the coin is fair, meaning we believe the proportion $p$ is centered around 0.5. To reflect this belief, we will choose a Beta distribution with parameters $\\alpha = 2$ and $\\beta = 2$. This distribution is symmetric and centered around 0.5, indicating our prior belief that the coin is likely fair but allowing for some uncertainty.\n",
+    "\n",
+    "To implement this in our analysis, we will instantiate a new `BayesianProportionEstimator` object, `B`. This object encapsulates our prior belief on `p` and provides the framework for updating this belief based on observed data. \n",
+    "\n",
+    "Here's how we can set this up in code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "B = BayesianProportionEstimator(prior=Beta(2, 2))\n",
+    "# alternative instantiation:\n",
+    "B = BayesianProportionEstimator(prior_alpha=2, prior_beta=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the plot function to visualize the probability density function (PDF) of our prior distribution. This visualization helps us understand our initial belief about `p`. By plotting the PDF of the $Beta(2, 2)$ distribution, we can see how our belief is symmetrically distributed around the value 0.5, reflecting our prior conviction that the coin is fair with some allowance for variability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='x', ylabel='pdf(x)'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "B.prior.plot(\"pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Likelihood and Posterior\n",
+    "\n",
+    "The likelihood function represents the probability of observing the data given the parameter $p$. For coin tosses, this is described by the Binomial distribution. If we observe $k$ heads out of $n$ tosses, the likelihood is given by:\n",
+    "\n",
+    "$$\\text{Binomial}(k | n, p) = \\binom{n}{k} p^k (1 - p)^{n - k}$$\n",
+    "\n",
+    "Using Bayes' theorem, we combine the prior distribution with the likelihood of the observed data to obtain the posterior distribution, which represents our updated beliefs about $p$ after observing the data. \n",
+    "\n",
+    "\n",
+    "Because the Beta distribution is a conjugate prior to the Binomial likelihood, the posterior distribution is also a Beta distribution with updated parameters. That is, if the prior is $\\text{Beta}(\\alpha, \\beta)$ and we observe $k$ heads in $n$ tosses, the posterior distribution is:\n",
+    "\n",
+    "$$\\text{Beta}(\\alpha + k, \\beta + n - k)$$\n",
+    "\n",
+    "This means that we simply add the number of observed heads to $\\alpha$ and the number of observed tails to $\\beta $ to get the parameters of the posterior distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the `BayesianProportionEstimator`, the process of updating our prior belief with observed data is straightforward and can be accomplished in a single line of code. We simply use the fit method on our data.\n",
+    "\n",
+    "However, to maintain compatibility with the estimator's API, we need to supply an additional argument, `X`, which is expected to be a DataFrame or Series of the same length as `y`. This X parameter will be ignored during the fitting process, so for simplicity, we can simply set `X` to be the same as `y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a {color: black;background-color: white;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a pre{padding: 0;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-toggleable {background-color: white;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-estimator:hover {background-color: #d4ebff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-item {z-index: 1;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-parallel-item:only-child::after {width: 0;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-297ef14b-1abb-440b-8a95-bc86cb109b5a div.sk-text-repr-fallback {display: none;}</style><div id='sk-297ef14b-1abb-440b-8a95-bc86cb109b5a' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('549ac188-3b26-40b5-a294-27556a286ecd') type=\"checkbox\" ><label for=UUID('549ac188-3b26-40b5-a294-27556a286ecd') class='sk-toggleable__label sk-toggleable__label-arrow'>BayesianProportionEstimator</label><div class=\"sk-toggleable__content\"><pre>BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('1af05224-7c51-4040-832a-7473aa43fa44') type=\"checkbox\" ><label for=UUID('1af05224-7c51-4040-832a-7473aa43fa44') class='sk-toggleable__label sk-toggleable__label-arrow'>Beta</label><div class=\"sk-toggleable__content\"><pre>Beta(alpha=2, beta=2)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = y.copy()\n",
+    "B.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, after fitting the BayesianProportionEstimator to our observed data, we obtain a posterior distribution represented by the Beta distribution with updated parameters $\\alpha=10$ and $\\beta=4$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc {color: black;background-color: white;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc pre{padding: 0;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-toggleable {background-color: white;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-estimator:hover {background-color: #d4ebff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-item {z-index: 1;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-parallel-item:only-child::after {width: 0;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc div.sk-text-repr-fallback {display: none;}</style><div id='sk-10b0171d-e28e-47dc-b327-fe3c6a1c06fc' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Beta(alpha=10, beta=4)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('b54fcb5d-465b-441a-baaf-0f6d85ba2a45') type=\"checkbox\" checked><label for=UUID('b54fcb5d-465b-441a-baaf-0f6d85ba2a45') class='sk-toggleable__label sk-toggleable__label-arrow'>Beta</label><div class=\"sk-toggleable__content\"><pre>Beta(alpha=10, beta=4)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Beta(alpha=10, beta=4)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B._posterior"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A plot of this posterior shows that it skews to the right with a mean of around 0.71, reflecting our updated belief that the coin is more likely to land on heads than tails. This skewness arises from the combination of our prior belief and the observed data, which had a higher number of heads compared to tails."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7142857142857143"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B._posterior.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='x', ylabel='pdf(x)'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "B._posterior.plot(\"pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Updating\n",
+    "\n",
+    "A significant advantage of the Bayesian approach is its ability to incorporate new data and continuously update our posterior distribution. Suppose we conduct 100 additional coin tosses and observe that, unexpectedly, 80 of these new tosses result in tails:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     0\n",
+       "1     0\n",
+       "2     0\n",
+       "3     0\n",
+       "4     0\n",
+       "     ..\n",
+       "95    0\n",
+       "96    0\n",
+       "97    1\n",
+       "98    0\n",
+       "99    0\n",
+       "Name: head, Length: 100, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the number of ones and zeros\n",
+    "ones = [1] * 20\n",
+    "zeros = [0] * 80\n",
+    "\n",
+    "# Combine them into a single list\n",
+    "data = ones + zeros\n",
+    "\n",
+    "# Shuffle the list to randomize the order\n",
+    "np.random.shuffle(data)\n",
+    "\n",
+    "# Create a Series from the shuffled list\n",
+    "y2 = pd.Series(data, name=\"head\")\n",
+    "\n",
+    "y2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This new data can be integrated into our existing model to further refine our belief about the coin's fairness. We do so using the `update` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 {color: black;background-color: white;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 pre{padding: 0;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-toggleable {background-color: white;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-estimator:hover {background-color: #d4ebff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-item {z-index: 1;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-parallel-item:only-child::after {width: 0;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-dc7a851b-bff0-4514-94bf-be7c00e7da49 div.sk-text-repr-fallback {display: none;}</style><div id='sk-dc7a851b-bff0-4514-94bf-be7c00e7da49' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('53455b22-f320-42be-ba4e-e697b06ac313') type=\"checkbox\" ><label for=UUID('53455b22-f320-42be-ba4e-e697b06ac313') class='sk-toggleable__label sk-toggleable__label-arrow'>BayesianProportionEstimator</label><div class=\"sk-toggleable__content\"><pre>BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('aa21ea68-8bb5-4aaa-9c54-08b784fd5822') type=\"checkbox\" ><label for=UUID('aa21ea68-8bb5-4aaa-9c54-08b784fd5822') class='sk-toggleable__label sk-toggleable__label-arrow'>Beta</label><div class=\"sk-toggleable__content\"><pre>Beta(alpha=2, beta=2)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "BayesianProportionEstimator(prior=Beta(alpha=2, beta=2), prior_alpha=2,\n",
+       "                            prior_beta=2)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = y2\n",
+    "B.update(X, y2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that after this update, the posterior becomes $\\text{Beta}(\\alpha=30, \\beta=84)$, which is a left-skewing distribution with a mean of 0.263."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-b5847b09-8dc9-44da-914b-febcda505010 {color: black;background-color: white;}#sk-b5847b09-8dc9-44da-914b-febcda505010 pre{padding: 0;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-toggleable {background-color: white;}#sk-b5847b09-8dc9-44da-914b-febcda505010 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b5847b09-8dc9-44da-914b-febcda505010 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-b5847b09-8dc9-44da-914b-febcda505010 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b5847b09-8dc9-44da-914b-febcda505010 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-item {z-index: 1;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-parallel-item:only-child::after {width: 0;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-b5847b09-8dc9-44da-914b-febcda505010 div.sk-text-repr-fallback {display: none;}</style><div id='sk-b5847b09-8dc9-44da-914b-febcda505010' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Beta(alpha=30, beta=84)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('7f4b07f3-40bf-4926-972b-a0b5ae355c8c') type=\"checkbox\" checked><label for=UUID('7f4b07f3-40bf-4926-972b-a0b5ae355c8c') class='sk-toggleable__label sk-toggleable__label-arrow'>Beta</label><div class=\"sk-toggleable__content\"><pre>Beta(alpha=30, beta=84)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Beta(alpha=30, beta=84)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B._posterior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2631578947368421"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "B._posterior.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='x', ylabel='pdf(x)'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "B._posterior.plot(\"pdf\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3e631b8a076cc106144e9b132b7d31cae2f1e2660b47e5f9fcb0397caae5fbd5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/skpro/regression/bayesian_proportion.py b/skpro/regression/bayesian_proportion.py
new file mode 100644
index 000000000..e638fa58a
--- /dev/null
+++ b/skpro/regression/bayesian_proportion.py
@@ -0,0 +1,172 @@
+"""Bayesian proportion estimator for probabilistic regression."""
+
+__author__ = ["meraldoantonio"]
+
+from skpro.distributions import Beta
+from skpro.regression.base import BaseProbaRegressor
+
+
+class BayesianProportionEstimator(BaseProbaRegressor):
+    """Bayesian probabilistic estimator for proportions.
+
+    This estimator uses a Beta prior and Beta posterior with a Binomial likelihood
+    for Bayesian inference of proportions. It provides methods for updating the
+    posterior, making predictions, and various utilities for analysis and visualization.
+
+    """
+
+    _tags = {
+        # packaging info
+        # --------------
+        "authors": ["meraldoantonio"],
+        "python_dependencies": ["scipy", "matplotlib"],
+        "capability:multioutput": False,
+        "capability:missing": True,
+        # estimator tags
+        # --------------
+        "capability:multioutput": False,  # can the estimator handle multi-output data?
+        "capability:missing": True,  # can the estimator handle missing data?
+        "X_inner_mtype": "pd_DataFrame_Table",  # type seen in internal _fit, _predict
+        "y_inner_mtype": "pd_Series_Table",  # type seen in internal _fit
+    }
+
+    def __init__(self, prior_alpha=None, prior_beta=None, prior=None):
+        """Initialize the Bayesian inference class with priors.
+
+        Parameters
+        ----------
+        prior_alpha : float, optional
+            The alpha parameter for the Beta prior distribution. Default is None.
+        prior_beta : float, optional
+            The beta parameter for the Beta prior distribution. Default is None.
+        prior : Beta, optional
+            An existing Beta distribution prior. Default is None.
+
+        Raises
+        ------
+        ValueError
+            If neither (prior_alpha and prior_beta) nor prior are provided.
+        TypeError
+            If the provided prior is not an instance of Beta.
+        """
+        if prior is None:
+            if prior_alpha is None or prior_beta is None:
+                raise ValueError(
+                    "Must provide either (prior_alpha and prior_beta) or prior."
+                )
+            self.prior_alpha = prior_alpha
+            self.prior_beta = prior_beta
+            self.prior = Beta(alpha=prior_alpha, beta=prior_beta)
+        else:
+            if not isinstance(prior, Beta):
+                raise TypeError("Prior must be an instance of Beta.")
+            self.prior = prior
+            self.prior_alpha = prior.alpha
+            self.prior_beta = prior.beta
+
+        super().__init__()
+
+    def _fit(self, X, y):
+        """Fit regressor to the observed data.
+
+        Writes to self:
+            Sets fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to;
+            will be ignored
+        y : pandas Series, must be same length as X
+            represents a series of binary experiments
+            whose outcome are either True (1) or False (0)
+
+        Returns
+        -------
+        self : reference to self
+        """
+        assert y.apply(
+            lambda x: isinstance(x, bool) or x in [0, 1]
+        ).all(), "Values in y must be boolean or convertible to boolean (0 or 1)"
+        self._posterior = self._perform_bayesian_inference(self.prior, X, y)
+        return self
+
+    def _predict_proba(self, X=None):
+        """Predict distribution over labels for data from features.
+
+        Parameters
+        ----------
+        X : pandas DataFrame, must have same columns as X in `fit`
+            data to predict labels for;
+            will be ignored
+
+        Returns
+        -------
+        y_pred : skpro BaseDistribution, same length as `X`
+            labels predicted for `X`
+        """
+        y_pred = Beta(alpha=self._posterior.alpha, beta=self._posterior.beta)
+        return y_pred
+
+    def _perform_bayesian_inference(self, prior, X, y):
+        """Perform Bayesian inference using a conjugate prior (Beta distribution).
+
+        This method calculates the posterior Beta distribution parameters
+        given observed binary outcomes.
+
+        Parameters
+        ----------
+        prior : Beta
+            The prior Beta distribution from skpro distributions.
+        X : pandas DataFrame
+            Feature data corresponding to the observed outcomes `y`.
+        y : array-like, must be binary (0 or 1)
+            Observed binary outcomes.
+
+        Returns
+        -------
+        posterior : Beta
+            The posterior Beta distribution with updated parameters.
+        """
+        n = len(y)
+        successes = y.sum()
+        posterior_alpha = prior.alpha + successes
+        posterior_beta = prior.beta + n - successes
+        return Beta(alpha=posterior_alpha, beta=posterior_beta)
+
+    def update(self, X, y):
+        """Update the posterior with new data.
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            New feature instances
+        y : pandas Series
+            New labels
+
+        Returns
+        -------
+        self : reference to self
+        """
+        self._posterior = self._perform_bayesian_inference(self._posterior, X, y)
+        return self
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+        """
+        params1 = {"prior_alpha": 1, "prior_beta": 1}
+        params2 = {"prior_alpha": 2, "prior_beta": 2}
+
+        return [params1, params2]