Introduction: A Dirichlet process (DP) is a distribution over probability distributions. We generally think of distributions as defined over numbers of some sort (real numbers, non-negative integers etc.), so at first it may seem a little exotic to talk about distributions over distributions. If you feel that way at this point, one obvious but very reassuring fact that we would like to point out is that probability theory still applies to these objects, however exotic they may seem initially. So, as we will see shortly, it is quit
In the last couple of lectures, in our study of Bayesian nonparametric approaches, we considered the...
Increasing additive processes (IAP), i.e. processes with positive independent increments, represent ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
The Dirichlet process has been extensively studied over the last thirty years, along with various ge...
U ovom radu uvodimo pojam Dirichletovog procesa u kontekstu bayesovske statistike. Navedeni su osnov...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
The present paper provides a review of the results concerning distributional properties of means of ...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
Discrete random probability measures and the exchangeable random partitions they induce are key tool...
Nonparametric Bayesian models are commonly used to obtain robust statistical inference, and the most...
The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is inc...
The Dirichlet distribution appears in many areas of application, which include modelling of composit...
In the last couple of lectures, in our study of Bayesian nonparametric approaches, we considered the...
Increasing additive processes (IAP), i.e. processes with positive independent increments, represent ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...
This book focuses on the properties associated with the Dirichlet process, describing its use a prio...
The Dirichlet process has been extensively studied over the last thirty years, along with various ge...
U ovom radu uvodimo pojam Dirichletovog procesa u kontekstu bayesovske statistike. Navedeni su osnov...
Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recentl...
The present paper provides a review of the results concerning distributional properties of means of ...
A family of nonparametric prior distributions which extends the Dirichlet process is introduced and ...
This paper considers a generalization of the Dirichlet process which is obtained by suitably normali...
The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spa...
Discrete random probability measures and the exchangeable random partitions they induce are key tool...
Nonparametric Bayesian models are commonly used to obtain robust statistical inference, and the most...
The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is inc...
The Dirichlet distribution appears in many areas of application, which include modelling of composit...
In the last couple of lectures, in our study of Bayesian nonparametric approaches, we considered the...
Increasing additive processes (IAP), i.e. processes with positive independent increments, represent ...
This book presents a systematic and comprehensive treatment of various prior processes that have bee...