Bayesian data analysisBayesian statistical decision theoryA Bayesian solution is presented to the problem of straight-line fitting when both variables x and y are subject to error. The solution, which is fully symmetric with respect to x and y, contains a very surprising feature: it requires a ...
The main idea of Bayesian analysis is simple and intuitive. There are some data to be explained, and we have a set of candidate explanations. Before knowing the new data, the candidate explanations have some prior credibilities of being the best explanation. Then, when given the new data, ...
Bayesian Data Analysis-英文文献.pdf,Statistical Science 2009, Vol. 24, No. 2, 176–178 DOI: 10.1214/09-STS284D Main article DOI: 10.1214/09-STS284 © Institute of Mathematical Statistics, 2009 Bayes, Jeffreys, Prior Distributions and the Philosophy of
Bayesian analysistechniquesprovide a formal method for integration of prior knowledge drawn from other imaging methods. In pure form, Bayesian techniques estimate a posterior probability distribution (a form of solution) based on the experimental data and prior knowledge expressed in the form of a prob...
The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and acti... JM Rehg,KP Murphy,PW Fieguth - IEEE Computer Society Conference on Computer Vision & Pattern Recognition 被引量: 108发表: 2002年 Bayesian...
Our support consistency guarantee for the constrained Type-II ML solution extends to any global solution of the multiple sparse Bayesian learning (M-SBL) optimization whose nonzero coefficients lie inside a bounded interval. Our analysis ... S Khanna,CR Murthy 被引量: 6发表: 2017年 Optimizing ...
An easy solution to that is to increase the number of warmup iterations, during warmup the sampler is tuning itself to the parameter and likelihood space, longer warmup means that the sampler will have more time to develop efficient rules to sample the posterior. So we can try with...
We construct a model and propose solution procedures for both stable and unstable environments. It is shown that when the environment is stable, the decision problem is equivalent to a classification problem. When it is unstable, we formulate the problem as a two-person zero-sum game. In both...
We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to ...
modeling. Leaving aside the oft-reported benefits of Bayesian analysis, we primarily used this environment because the proposed B-Ianus model requires high-dimensional integration with no known analytical solution. In other words, we do not have mathematical optimizers for the likelihood functions with...