11_temporal_probability_models #17
Conversation
| - [Conclusion](#conclusion) | ||
| - [Resources](#resources) | ||
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| # Introduction |
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the intro is a bit too brief, elaborate.
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| Intuitively, the probability of rain increases from day 1 to day 2 because rain persists. | ||
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| ## Prediction |
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try to make different sections of your lecture note more relevant to one another; don't jump from a topic to another without proper preparation
| - The process of discovering the sequence of hidden states, given the sequence of observations, is known as decoding or inference. The **Viterbi** algorithm is commonly used for decoding. | ||
| - The parameters of an HMM are the A transition probability matrix and the B observation likelihood matrix. Both can be trained with the **forward-backward** algorithm. | ||
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| # Resources |
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it would be good to tell which reference is for which section
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| | *Error of DBN particle filtering.* | | ||
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| # Conclusion |
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have a more detailed conclusion, the body of your notebook is very long, and I think some important points must be missing here
| return S | ||
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| ## Useful links |
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I think it's better to add a "useful links" section at the end of your notebook and mention all the links there, but that's your choice (just a suggestion)
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| | *Figure 2* | | ||
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| # Dynamic Bayes Nets |
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review your english since this is pretty much formal... I will mention a couple of examples in this section
| | *Figure 2* | | ||
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| # Dynamic Bayes Nets | ||
| A Bayesian network is a snapshot of the system at a given time and is used to model systems that are in some kind of equilibrium state. Unfortunately, most systems in the world change over time and sometimes we are interested in how these systems evolve over time more than we are interested in their equilibrium states. Whenever the focus of our reasoning is change of a system over time, we need a tool that is capable of modeling dynamic systems. |
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"A Bayesian network is a snapshot of the system at a given time and it's used to model systems in some kind of equilibrium state. Unfortunately, most systems in the world change over time, and sometimes we are more interested in how these systems evolve than in their equilibrium states. Whenever the focus of our reasoning is the change of a system over time, we need a tool capable of modeling dynamic systems."
You use "that" very often it's kinda disturbing :(
| # Dynamic Bayes Nets | ||
| A Bayesian network is a snapshot of the system at a given time and is used to model systems that are in some kind of equilibrium state. Unfortunately, most systems in the world change over time and sometimes we are interested in how these systems evolve over time more than we are interested in their equilibrium states. Whenever the focus of our reasoning is change of a system over time, we need a tool that is capable of modeling dynamic systems. | ||
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| A dynamic Bayesian network (DBN) is a Bayesian network extended with additional mechanisms that are capable of modeling influences over time. The temporal extension of Bayesian networks does not mean that the network structure or parameters changes dynamically, but that a dynamic system is modeled. In other words, the underlying process, modeled by a DBN, is stationary. A DBN is a model of a stochastic process. |
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"that are" :((((
also "network structure or parameters changes" is incorrect...
- introduction added - conclusion added - psudocode changed to python
Particle Filtering Updated
Robot localization
Hi:)
Github does not support math equations in markdown files.
Please open the .md file in an original markdown viewer. (Or simply check this file)
Thanks!