Included in this distribution is matlab code to generate posterior samples for linear Gaussian and binary matrix factorization (noisy-or) Indian Buffet Process models. Three different posterior sampling algorithms are provided: Gibbs, reversible jump Markov chain Monte Carlo (RJMCMC), and sequential importance sampling (SIS). Only the Gibbs and SIS samplers are provided for the linear Gaussian IBP models.

ibp
...\linear_gaussian_model
...\.....................\test.m
...\.....................\face_data.m
...\.....................\face_data_zm.m
...\.....................\generate_test_data.m
...\.....................\hyper_sampler.m
...\.....................\hyper_sampler_dont_sample_hypers.m
...\.....................\linear_gaussian_model.m
...\.....................\logPX.m
...\.....................\logPXZ.m
...\.....................\particle_filter.m
...\.....................\particle_filter_for_faces.m
...\.....................\pf_est_post.m
...\.....................\resample.m
...\.....................\sampZ.m
...\.....................\DIGITSDATA.mat
...\.....................\FACEDATA.mat
...\.....................\PF-out-100.mat
...\finite
...\......\cannonize.m
...\......\sampY.m
...\......\sampZ.m
...\......\logPXYZ.m
...\......\clean.m
...\......\generate_test_data.m
...\......\inferstats.m
...\......\sampler.m
...\ibp_generate.m
...\cannonize.m
...\sampZ_finite.m
...\sampY.m
...\sampZ.m
...\logPZ.m
...\rjmcmc_sampler.m
...\plot_ibp_matrices.m
...\plot_graph.m
...\plot_circle.m
...\sampler.m
...\particle_filter.m
...\resample.m
...\hyper_sampler.m
...\plot_and_save_nips_graphs.m
...\logPXYZ.m
...\sampY_newrows_only.m
...\test.m
...\inferstats.m
...\generate_test_data.m
...\clean.m
...\secs2hmsstr.m
...\README

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