Latent Stick-Breaking Processes.
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We develop a model for stochastic processes with random marginal distributions. Our model relies on a stick-breaking construction for the marginal distribution of the process, and introduces dependence across locations by using a latent Gaussian copula model as the mechanism for selecting the atoms. The resulting latent stick-breaking process (LaSBP) induces a random partition of the index space, with points closer in space having a higher probability of being in the same cluster. We develop an efficient and straightforward Markov chain Monte Carlo (MCMC) algorithm for computation and discuss applications in financial econometrics and ecology. This article has supplementary material online.
Random probability measure
Random stochastic processes
Published Version (Please cite this version)10.1198/jasa.2010.tm08241
Publication InfoRodríguez, Abel; Dunson, David B; & Gelfand, Alan E (2010). Latent Stick-Breaking Processes. J Am Stat Assoc, 105(490). pp. 647-659. 10.1198/jasa.2010.tm08241. Retrieved from https://hdl.handle.net/10161/4401.
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David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties
Alan E. Gelfand
James B. Duke Distinguished Professor Emeritus of Statistical Science
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