Tags » Bayesian
Bayesian data analysis is not only about computing a posterior distribution,and Bayesian visualization is about more than trace plots of Markov chains.Rather, practical Bayesian data analysis is an iterative process of modelbuilding, inference, model checking and evaluation, and model expansion.Visualization is not only helpful in each of these stages of the Bayesianworkflow, it is indispensable for making inferences from the intricate,high-dimensional models of interest to applied researchers, and essential forunderstanding and diagnosing the increasingly complex algorithms required tofit such models.
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
We conduct an extensive set of experiments on 68 standard datasets anddemonstrate that our resulting classifiers perform very competitively withRandom Forest in terms of prediction, while keeping the out-of-core capabilityand superior classification time.
A key sticking point of Bayesian analysis is the choice of priordistribution, and there is a vast literature on potential defaults includinguniform priors, Jeffreys’ priors, reference priors, maximum entropy priors, andweakly informative priors. 69 more words
This is a somewhat belated introduction of a package that we published on CRAN at the beginning of the year already, but I hadn’t found the time to blog about this earlier. 534 more words