@nanmeng 2016-05-05T08:02:20.000000Z 字数 1433 阅读 1205

# Probabilistic Graphical Models(Stanford) - 2

notes Probabilistic_Graphical_Models

## Week1 Template Models

### 1. Overview of Template Models

There are some ''patterns'' are sharing between models within this model.  • sharing across pixel
• sharing pair of supperpixels
• sharing between and within a model

examples:  Template Variables: Template Models: Which of the following are advantages of using template models?

• CPDs in template models can often be copied many times.
• Template models can often capture events that occur in a time series.
• Template models can capture parameter sharing within a model.

### 2. Template Models - DBNs #### Markov Assumption The equation above do not make any assumption!!! we only re-expressing the
probability distribution in the way that time flows forward which means that the next step is independent of the past given the present. the equation above is acquired based on the independent assumption
An example of showing that the Markov assumption sometimes is too strong. #### Time Invariance Time invariance states that the states of system change from one to another is independent of the current time.
we can enrich the model by including some other conditions.   #### Dynamic Bayesian Network  #### Summary ### 3. Temporal Models - HMMs Numerous Application

• Robot Localization • Speech Recognition  Phones matters much(Phonetic Alphabet).
• Word HMM   this is the whole system.

#### Summary ### 4. Plate Models

$\theta$ is the actual CPD parameter
$\theta$ is outside the plate which means it is not indexed by $t$ which also means it is the same for all values of $t$.  #### Nested Plates

The variable in the nested plates are indexed by both. #### Overlapping Plates  #### Plate Dependency Model  #### Summary   • 私有
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