Hyperprior on symmetric Dirichlet distribution
نویسنده
چکیده
In this article we introduce how to put vague hyperprior on Dirichlet distribution, and we update the parameter of it by adaptive rejection sampling (ARS). Finally we analyze this hyperprior in an over-fitted mixture model by some synthetic experiments.
منابع مشابه
Estimating Functions of Distributions Defined over Spaces of Unknown Size
We consider Bayesian estimation of information-theoretic quantities from data, using a Dirichlet prior. Acknowledging the uncertainty of the event space size m and the Dirichlet prior’s concentration parameter c, we treat both as random variables set by a hyperprior. We show that the associated hyperprior, P (c,m), obeys a simple “Irrelevance of Unseen Variables” (IUV) desideratum iff P (c,m) =...
متن کاملHierarchical Variational Models (Appendix)
Relationship to empirical Bayes and RL. The augmentation with a variational prior has strong ties to empirical Bayesian methods, which use data to estimate hyperparameters of a prior distribution (Robbins, 1964; Efron & Morris, 1973). In general, empirical Bayes considers the fully Bayesian treatment of a hyperprior on the original prior—here, the variational prior on the original meanfield—and...
متن کاملA hierarchical Bayesian model for calibrating estimates of species divergence times.
In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to qu...
متن کاملSome Diffusion Processes Associated With Two Parameter Poisson-Dirichlet Distribution and Dirichlet Process
The two parameter Poisson-Dirichlet distribution PD(α, θ) is the distribution of an infinite dimensional random discrete probability. It is a generalization of Kingman’s Poisson-Dirichlet distribution. The two parameter Dirichlet process Πα,θ,ν0 is the law of a pure atomic random measure with masses following the two parameter Poisson-Dirichlet distribution. In this article we focus on the cons...
متن کاملBayesian inference of structural brain networks with region-specific Dirichlet parametrisation
In this paper we present an extension to a Bayesian framework for inference of structural brain networks. This framework provides a generative model that explicitely describes how structural brain networks lead to observed streamline distributions. Our extension consists of adding a hyperprior on the latent Dirichlet variables, such that we can capture global and region-specific behaviour withi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1708.08177 شماره
صفحات -
تاریخ انتشار 2017