文件名称:ibp
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介绍说明－－下载内容均来自于网络，请自行研究使用
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.-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.
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.-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.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
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