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Everything about Dirichlet process
Family of stochastic processes
In probability theory, Dirichlet processes are a family of stochastic processes whose realizations are probability distributions. In other words, a Dirichlet process is a probability distribution whose range is itself a set of probability distributions. It is often used in Bayesian inference to describe the prior knowledge about the distribution of random variables—how likely it is that the random variables are distributed according to one or another particular distribution. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Dirichlet processA 5-minute orientation
- Skim a recent paper: The Dependent Dirichlet Process and Related ModelsInfluential work since 2022
- Pick up a classic book: Semi-Dirichlet Forms and Markov ProcessesThe most-reprinted book on the subject
- Read the landmark paper: Markov Chain Sampling Methods for Dirichlet Process Mixture ModelsCited 2,714 times
- Go sideways: Hierarchical Dirichlet processA closely related topic
- Go sideways: Variational Bayesian methodsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Markov Chain Sampling Methods for Dirichlet Process Mixture Models
Variational inference for Dirichlet process mixtures
Estimating Mixture of Dirichlet Process Models
The Nested Dirichlet Process
Generalized Spatial Dirichlet Process Models
A tutorial on Dirichlet process mixture modeling
A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling
Dirichlet Process Mixed Generalized Linear Models
🆕 Influential since 2022
The Dependent Dirichlet Process and Related Models
Clustering consistency with Dirichlet process mixtures
mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm
Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder
📚 Books worth reading
- 📕Semi-Dirichlet Forms and Markov ProcessesYoichi Oshima · 2013
Pseudo Differential Operators & Markov ProcessesNiels Jacob · 1996
Introduction to the theory of (non-symmetric) Dirichlet formsZhi-Ming Ma · 1992
Dirichlet Forms Methods for Poisson Point Measures and Lévy ProcessesNicolas Bouleau, Laurent Denis · 2015
Masatoshi FukushimaNiels Jacob · 2010
Dirichlet forms and stochastic processesZhi-Ming Ma, China) International Conference on Advances in Structural Dynamics (2000 : Hong Kong · 1995
The theory of generalized Dirichlet forms and its applications in analysis and stochasticsWilhelm Stannat · 1999
Pseudo differential operators & Markov processesNiels Jacob, N. Jacob · 2002
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Hierarchical Dirichlet process In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a…
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