Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an ...

We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved two-state indicator variable that follows a Markov ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...

This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics ...

Tohoku University researchers have developed optimized quantum sensor networks using superconducting qubits to enhance the ...