The combinatorial structure of beta negative binomial processes
DOI10.3150/15-BEJ729zbMath1358.60069arXiv1401.0062MaRDI QIDQ726741
Daniel M. Roy, Creighton Heaukulani
Publication date: 14 July 2016
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1401.0062
Bayesian nonparametricsMarkov chain Monte Carlo algorithmIndian buffet processmultisetsbeta negative binomial processeslatent feature models
Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Numerical analysis or methods applied to Markov chains (65C40) Stochastic processes (60G99) Random measures (60G57) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Exchangeability for stochastic processes (60G09)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Feature allocations, probability functions, and paintboxes
- Nonparametric Bayes estimators based on beta processes in models for life history data
- Negative binomial distributions for point processes
- Generalized hypergeometric, digamma and trigamma distributions
- Slice sampling. (With discussions and rejoinder)
- Nonparametric Bayesian estimators for counting processes
- Completely random measures
- Bayesian nonparametric statistical inference for Poisson point processes
- Poisson/gamma random field models for spatial statistics
- Negative binomial processes
- Cluster and feature modeling from combinatorial stochastic processes