probability theory, Dirichlet processes (after the distribution associated with Peter Gustav Lejeune Dirichlet) are a family of stochastic processes whose realizations...

the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of...

infinite-dimensional generalization of the Dirichlet distribution is the Dirichlet process. The Dirichlet distribution of order K ≥ 2 with parameters...

In natural language processing, latent Dirichlet allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) for modeling automatically...

Johann Peter Gustav Lejeune Dirichlet (/ˌdɪərɪˈkleɪ/; German: [ləˈʒœn diʁiˈkleː]; 13 February 1805 – 5 May 1859) was a German mathematician. In number...

In probability theory and statistics, the Dirichlet process (DP) is one of the most popular Bayesian nonparametric models. It was introduced by Thomas...

dependent Dirichlet process (DDP) provides a non-parametric prior over evolving mixture models. A construction of the DDP built on a Poisson point process. The...

Dirichlet Distribution -- Polya Restaurant Scheme and Chinese Restaurant Process". Xinhua Zhang, "A Very Gentle Note on the Construction of Dirichlet...

becomes the Dirichlet process. The discount parameter gives the Pitman–Yor process more flexibility over tail behavior than the Dirichlet process, which has...

individuals in the dataset Dirichlet process, a stochastic process corresponding to an infinite generalization of the Dirichlet distribution. Dynamic programming...

previously described hidden Markov models with Dirichlet priors uses a Dirichlet process in place of a Dirichlet distribution. This type of model allows for...

In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite...

statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in...

a probability distribution, such as the symmetric Dirichlet distribution and the Dirichlet process. The rest of this article focuses on the latter usage...

Brownian motion Chinese restaurant process CIR process Continuous stochastic process Cox process Dirichlet processes Finite-dimensional distribution First...

statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used to describe...

In mathematics, the Dirichlet–Jordan test gives sufficient conditions for a complex-valued, periodic function f {\displaystyle f} to be equal to the sum...

(GMMs). mclust is an R package for mixture modeling. dpgmm Pure Python Dirichlet process Gaussian mixture model implementation (variational). Gaussian Mixture...

equations) Dirichlet process Dependent Dirichlet process Hierarchical Dirichlet process Imprecise Dirichlet process Dirichlet ring (number theory) Dirichlet series...

function) and functional analysis, Dirichlet forms generalize the Laplacian (the mathematical operator on scalar fields). Dirichlet forms can be defined on any...

In number theory, Dirichlet's theorem, also called the Dirichlet prime number theorem, states that for any two positive coprime integers a and d, there...

commonly called Dirichlet's box principle or Dirichlet's drawer principle after an 1834 treatment of the principle by Peter Gustav Lejeune Dirichlet under the...

probability distributions of amino acids developed from a hierarchical Dirichlet process model". PLOS Computational Biology. 6 (4): e1000763. Bibcode:2010PLSCB...

original developers of deep belief networks and of hierarchical Dirichlet processes. Teh was a keynote speaker at Uncertainty in Artificial Intelligence...

non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the number of latent variables to grow as necessary to...

In mathematics, the Dirichlet boundary condition is imposed on an ordinary or partial differential equation, such that the values that the solution takes...

S2CID 1890561. Neal, Radford M. (2000). "Markov Chain Sampling Methods for Dirichlet Process Mixture Models". Journal of Computational and Graphical Statistics...

Voronoi decomposition, a Voronoi partition, or a Dirichlet tessellation (after Peter Gustav Lejeune Dirichlet). Voronoi cells are also known as Thiessen polygons...

statistics, a continuous-time stochastic process, or a continuous-space-time stochastic process is a stochastic process for which the index variable takes a...

In mathematical analysis, the Dirichlet kernel, named after the German mathematician Peter Gustav Lejeune Dirichlet, is the collection of periodic functions...