We propose a class of kernel stick-breaking processes for uncountable collections of
dependent random probability measures. The process is constructed by first introducing an …

We present new methodology to extend hidden Markov models to the spatial domain, and
use this class of models to analyze spatial heterogeneity of count data on a rare …

This chapter deals with likelihood inference for spatial point processes using the methods of
RA Moyeed and AJ Baddeley, CJ Geyer and EA Thompson, AE Gelfand and BP Carlin, CJ …

The Markov chain Monte Carlo method is a general technique for obtaining samples from a
probability distribution. In earlier work, we showed that for many applications one can modify …

As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose
a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of …

In dynamic statistical modeling situations, observations arise sequentially, causing the
model to expand by progressive incorporation of new data items and new unknown …

There is a lack of methodological results to design efficient Markov chain Monte Carlo
(MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters …

Markov chain Monte Carlo is a method of producing a correlated sample to estimate features
of a target distribution through ergodic averages. A fundamental question is when sampling …

A rich and flexible class of random probability measures, which we call stick-breaking priors,
can be constructed using a sequence of independent beta random variables. Examples of …

Partially observed Markov process (POMP) models, also known as hidden Markov models
or state space models, are ubiquitous tools for time series analysis. The R package pomp …