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Resources
- Old Mid-term Exams
- Midterm Exam, Spring'07
- Viterbi Decoding
- Viterbi Animation
- Viterbi Demo
- MCMC, Gibbs Sampling
- Gibbs Sampling with Example
- MCMC,Gibbs Intro
- JT Olds' Python Code
- Sampling Algorithms
Rejection Sampling
Likelihood Weighting
Gibbs Sampling
which require
bayesnets.py
bayestools.py
markovtools.py
To run, for example, the rejection sampling script for 1000 samples,
type the following:
./rejectionsampling.py 1000
- Exact Inference Algorithms
Sum-Product Algorithm
Max-Product
Algorithm
These are standalone, so to run, for example,
the sum-product algorithm, type
./sumproduct.py
These have good printouts to the screen so you can follow the methods.
The following two algorithms only print out the final solution, but
you can look at the source files for the methods.
Enumeration
Variable Elimination
- Filtering/Smoothing/Prediction and Viterbi Decoding
(Homework 2, Problem 4)
prob4.py
which requires
hmm_tools.py
hmms.py
Run by typing
./prob4.py
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