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Markov Random Field Priors for Univariate Density Estimation Markov Random Field Priors for Univariate Density Estimation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wolpert, Robert L. Laviney, Michael Lavine, M. |
| Copyright Year | 1997 |
| Abstract | We propose to model the unknown distribution F of a sequence of independent real-valued random variables by partitioning the real line into intervals I 0ors (MRFPs). We argue and illustrate that many commonly-expressed prior opinions about the shape and form of F can be expressed as statements about the joint distribution of neighboring p i 's, leading to simple MRFP expressions for prior beliefs that are awkward to express in other models. In particular, we will show how to model beliefs about log concavity, unimodality, and monotonicity. The posterior distributions of the p i 's in our models (and hence the approximate predictive distributions for subsequent observations) are readily computed using Markov chain Monte-Carlo methods. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ftp.isds.duke.edu/WorkingPapers/95-08.ps |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Concave function Markov chain Monte Carlo Markov random field Monte Carlo method |
| Content Type | Text |
| Resource Type | Article |
