Likelihoods

Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下本文摘要:Recent new methods in Bayesian simu

Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.

标签： Likelihoods Sequential Bayesian without

上传时间： 2016-05-26

上传用户：离殇
A Web Tutorial on Discrete Features of Bayes Decision Theory This applet allows for the calculation

A Web Tutorial on Discrete Features of Bayes Decision Theory This applet allows for the calculation of the decision boundary given a three dimensional feature vector. Specifically, by stipulating the variables such as the priors, and the conditional Likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.

标签： calculation Tutorial Discrete Decision

上传时间： 2013-12-22

上传用户：hxy200501