├── Graphical models, exponential families, and variational inference by Martin J. Wainwright and Michael I. Jordan.pdf ├── Modeling and Reasoning with Bayesian.pdf ├── Probabilistic Graphical Models - Principles and Techniques.pdf ├── README.md ├── book.PNG ├── books ├── Bayesian Reasoning and Machine Learning by David Barber.pdf └── Information Theory, Inference, and Learning Algorithms by David J. C. Mackay.pdf ├── cs228.PNG ├── exam ├── CS228 WINTER 2009 FINAL SOLUTION (1) Using ... - Stanford AI Lab.pdf ├── SOLUTION.MD ├── final-06.pdf ├── final-08.pdf ├── final040407.pdf └── final16_with_sols.pdf ├── gradescope.PNG └── hw ├── SOLUTION.MD ├── cs228v2 ├── README.md ├── exam_p6.py ├── hw2 │ ├── README │ ├── binary_tree.py │ ├── column.fa │ ├── factors.py │ ├── mrf_tree.py │ ├── multicolumn.fa │ ├── output_ABC.txt │ ├── output_DEFG.txt │ ├── solution.py │ ├── tree.nh │ └── treealt.nh ├── hw3 │ ├── README │ ├── cluster_graph.py │ ├── do_pa3.py │ ├── factors.py │ ├── images.mat │ ├── ldpc36-128.mat │ ├── ldpc36-1600.mat │ ├── output_A.txt │ ├── output_C.txt │ ├── output_DE.txt │ ├── output_FG.txt │ ├── parallel.py │ ├── plot_c.png │ ├── plot_d.png │ ├── plot_d_many_errors.png │ ├── plot_e_error008.png │ └── plot_e_error010.png ├── hw4 │ ├── README │ ├── noisy_10.png │ ├── noisy_10.txt │ ├── noisy_20.png │ ├── noisy_20.txt │ ├── orig.png │ ├── orig.txt │ ├── part_c_analysis.txt │ ├── part_c_neg.png │ ├── part_c_neg.txt │ ├── part_c_rand.png │ ├── part_c_rand.txt │ ├── part_c_same.png │ ├── part_c_same.txt │ ├── part_d_comparison_10.png │ ├── part_d_comparison_20.png │ ├── part_d_error_rates.txt │ ├── part_e_comparison_10.png │ ├── part_e_comparison_20.png │ ├── part_e_error_rates.txt │ ├── part_f_analysis.txt │ ├── part_f_frequencies.txt │ ├── part_f_histogram10.png │ ├── part_f_histogram20.png │ └── submission.py └── hw5 │ ├── solution.py │ └── util.py ├── hw1.pdf ├── hw1 ├── hw1.tex └── programming │ ├── a4.png │ ├── a5.png │ ├── a6_hist_corrupt.png │ ├── a6_hist_real.png │ ├── a7.png │ ├── pa1.py │ ├── q6.mat │ ├── q7.mat │ ├── submission │ └── pa1.py │ └── trained_mnist_model ├── hw2.pdf ├── hw2 ├── hw2.pdf ├── hw2.tex └── programming │ ├── data_helper.py │ ├── pa2.py │ └── tree.py ├── hw3.pdf ├── hw3 ├── hw3.tex └── programming │ ├── 5c.png │ ├── 5d_epsilon=10.png │ ├── 5d_epsilon=6.png │ ├── 5d_epsilon=8.png │ ├── 5fg_error=10.png │ ├── 5fg_error=6.png │ ├── do_pa3.py │ ├── factor_graph.py │ ├── factors.py │ ├── images.mat │ ├── ldpc36-128.mat │ └── ldpc36-1600.mat ├── hw4.pdf ├── hw4 ├── hw4.tex └── programming │ ├── denoised_10.png │ ├── denoised_20.png │ ├── denoised_dumb_10.png │ ├── denoised_dumb_20.png │ ├── f_10.png │ ├── f_20.png │ ├── log_neg │ ├── log_neg.png │ ├── log_rand │ ├── log_rand.png │ ├── log_same │ ├── log_same.png │ ├── main.py │ ├── noisy_10.png │ ├── noisy_10.txt │ ├── noisy_20.png │ ├── noisy_20.txt │ ├── orig.png │ ├── orig.txt │ ├── orig_img.png │ └── submission │ ├── main.py │ ├── noisy_10.txt │ ├── noisy_20.txt │ └── orig.txt ├── hw5.pdf ├── hw5 ├── hw5.tex └── starter │ ├── 2Bii.png │ ├── 2Biv.png │ ├── 2Bv.png │ ├── 2aii.png │ ├── starter.py │ ├── surveylabeled.dat │ └── surveyunlabeled.dat └── hwtemplate.tex /Graphical models, exponential families, and variational inference by Martin J. Wainwright and Michael I. Jordan.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kushagra06/CS228_PGM/HEAD/Graphical models, exponential families, and variational inference by Martin J. Wainwright and Michael I. 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