{"title":"\u3071\u3089\u3071\u3089\u3081\u304f\u308b\u300eDPpackage: Semi- and Non-parametric Modeling in R\u300f","categories":["\u3071\u3089\u3071\u3089\u3081\u304f\u308b\u30b7\u30ea\u30fc\u30ba","\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u30d9\u30a4\u30ba","R","DPpackage"],"author_url":"https://blog.hatena.ne.jp/ryamada22/","version":"1.0","provider_url":"https://hatena.blog","url":"https://ryamada22.hatenablog.jp/entry/20170716/1500074949","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fryamada22.hatenablog.jp%2Fentry%2F20170716%2F1500074949\" title=\"\u3071\u3089\u3071\u3089\u3081\u304f\u308b\u300eDPpackage: Semi- and Non-parametric Modeling in R\u300f - ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","height":"190","image_url":null,"blog_title":"ryamada\u306e\u907a\u4f1d\u5b66\u30fb\u907a\u4f1d\u7d71\u8a08\u5b66\u30e1\u30e2","width":"100%","description":"\u6587\u66f8 1. Introduction Semiparametric Bayes \u3068 Nonparametric Bayes (BSP\u3068BNP) Bayesian \u30a2\u30d7\u30ea\u3002R\u3067Dirichlet\u904e\u7a0b\u3092\u5b9f\u88c5\u3057\u3066\u3044\u308b\u306e\u306f(DPpackage\u306e\u4ed6\u306b)bayesm\u304c\u3042\u308b\u304c\u3001\u6271\u3048\u308b\u30e2\u30c7\u30eb\u306f\u9650\u5b9a\u7684 DPpackage\u304c\u5b9f\u88c5\u3057\u3066\u3044\u308b\u30e2\u30c7\u30eb DP mixtures of DP (Antoniak's MDP) DP mixtures (Lo etc.'s DPM) linear dependent DP (LDDP) linear dependent Poisson-DP (LDPD) weight depen\u2026","blog_url":"https://ryamada22.hatenablog.jp/","published":"2017-07-16 08:29:09","author_name":"ryamada22","type":"rich","provider_name":"Hatena Blog"}