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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Arts and Sciences Distinguished Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecol
James B. Duke Distinguished Emeritus Professor of Statistical Science
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