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Journal ArticleDOI

1. Bayesian Statistics 4

Jim Q. Smith
- 01 Mar 1993 - 
- Vol. 156, Iss: 2, pp 319-320
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This article is published in Journal of The Royal Statistical Society Series A-statistics in Society.The article was published on 1993-03-01. It has received 1214 citations till now. The article focuses on the topics: Bayesian statistics & Bayesian linear regression.

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Inference from Iterative Simulation Using Multiple Sequences

TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Journal ArticleDOI

emcee: The MCMC Hammer

TL;DR: The emcee algorithm as mentioned in this paper is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).
Journal ArticleDOI

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Journal ArticleDOI

General methods for monitoring convergence of iterative simulations

TL;DR: This work generalizes the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence.
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Logistic Regression in Rare Events Data

TL;DR: It is shown that more efficient sampling designs exist for making valid inferences, such as sampling all available events and a tiny fraction of nonevents, which enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables.
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