Author name: tirta

A Bayes optimal classifier is a statistical decision-making tool that minimizes expected loss or error in classification. It calculates the […]

Covariate empowered empirical Bayes (CEEB) is a method in Bayesian statistics that combines the strength of Empirical Bayes (EB) and

Bayesian Neural Networks (BNNs) are probabilistic models that capture uncertainty in predictions by modeling both aleatoric (data noise) and epistemic

Bayesian hypothesis testing, central to Bayesian statistics, employs Bayes’ theorem to evaluate the probability of alternative hypotheses in light of

Bayesian structural time series is a statistical technique used to analyze time-varying data. It combines elements of Bayesian statistics with

Hierarchical Bayesian models leverage hierarchical structures to refine prior distributions and improve Bayesian inference. By nesting parameters within a hierarchy,

Bayesian neural networks combine Bayesian modeling techniques with neural networks to enhance uncertainty quantification, model interpretability, and generative capabilities. By

Bayesian model averaging (BMA) is a technique that combines multiple Bayesian models to improve predictive accuracy. It involves weighting each

Bayesian belief networks (BBNs) are graphical models representing probabilistic relationships between variables. Nodes represent variables, and edges represent relationships. Conditional

Bayesian hierarchical models are multilevel models that incorporate uncertainty at multiple levels of parameters. They are defined by hyperparameters, which

Bayesian machine learning is a powerful approach to machine learning that utilizes probability distributions to represent uncertainty. It allows for

Hierarchical Bayesian modeling is a powerful statistical approach that incorporates hierarchical structures into Bayesian models. It allows for modeling complex