Name
Abstract | ||
|---|---|---|
In this paper, we consider many problems in Bayesian inference - from drawing samples to posteriors, to calculating confidence intervals, to implementing posterior matching algorithms, by finding maps that push one distribution to another. We show that for a large class of problems (with log-concave likelihoods and log-concave priors), these problems can be efficiently solved using convex optimization. We provide example applications within the context of dynamic statistical signal processing. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISIT.2013.6620628 | ISIT |
Keywords | Field | DocType |
belief networks,bayesian inference method,posterior matching algorithm,inference mechanisms,confidence interval calculation,maximum likelihood estimation,convex programming,dynamic statistical signal processing,optimal transport,convex optimization,computational modeling,convex functions,information theory,polynomials,monte carlo methods,markov processes | Mathematical optimization,Frequentist inference,Bayesian inference,Fiducial inference,Computer science,Statistical inference,Bayesian statistics,Prior probability,Proper convex function,Convex optimization | Conference |
ISSN | Citations | PageRank |
2157-8095 | 3 | 0.40 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanggyun Kim | 1 | 96 | 6.71 |
| Rui Ma | 2 | 24 | 3.95 |
| Diego Mesa | 3 | 5 | 1.78 |
| Todd P. Coleman | 4 | 199 | 23.98 |