منابع مشابه
Hierarchical Dirichlet Processes
Figure 1: Bayesian Mixture Model For a Bayesian Mixture Model as shown in figure 1, as k →∞, we shall have G = ∑∞ c=1 πcδφc , where all the φc are i.i.d. samples from G0, while the random sequence {πc}c=1 sum up to one, shall be constructed by the “Stick Breaking” process [3]. Suppose there is a stick with length 1. Let βc ∼ Beta(1, α) for c = 1, 2, 3, . . . , and regard them as fractions we ta...
متن کاملHierarchical Dirichlet Processes
We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well...
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Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), often used for non-parametric topic modeling, is an extension of DP for grouped data, where each group is a mixture over shared mixture densities. The Nested Dirichlet Process (nDP), on the o...
متن کاملHierarchical Dirichlet Processes with Random Effects
Data sets involving multiple groups with shared characteristics frequently arise in practice. In this paper we extend hierarchical Dirichlet processes to model such data. Each group is assumed to be generated from a template mixture model with group level variability in both the mixing proportions and the component parameters. Variabilities in mixing proportions across groups are handled using ...
متن کاملThe Infinite PCFG Using Hierarchical Dirichlet Processes
We present a nonparametric Bayesian model of tree structures based on the hierarchical Dirichlet process (HDP). Our HDP-PCFG model allows the complexity of the grammar to grow as more training data is available. In addition to presenting a fully Bayesian model for the PCFG, we also develop an efficient variational inference procedure. On synthetic data, we recover the correct grammar without ha...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2015
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2014.2318728