We already discuss how online learning works here using Binomial distribution with Beta distribution as conjugate prior distribution. During this post, we will try to use Gaussian distribution for online learning in Bayesian inference. 207 more words

## Tags » Bayesian

#### Covariances, Robustness, and Variational Bayes

Ryan Giordano, Tamara Broderick, Michael I. Jordan

Variational Bayes (VB) is an approximate Bayesian posterior inferencetechnique that is increasingly popular due to its fast runtimes on large-scaledatasets. 145 more words

#### Visualization in Bayesian workflow

Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, Andrew Gelman

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

Francois Petitjean, Wray Buntine, Geoffrey I. Webb, Nayyar Zaidi

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.

#### The prior can generally only be understood in the context of the likelihood

Andrew Gelman, Daniel Simpson, Michael Betancourt

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

#### The BayesianTools R package with general-purpose MCMC and SMC samplers for Bayesian statistics

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

#### Understanding Online/Sequential Learning in Bayesian Inference

After we understand the concept of Bernoulli, Binomial and Beta distribution we discuss here, we are ready to understand online learning used in Bayesian inference now. 613 more words