We observe n events occurring in (0, T] taken from a Poisson process. The intensity function of the process is assumed to be a step function with multiple changepoints. This article proposes a ...

Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

Computational statistics harnesses the power of sophisticated numerical algorithms and high‐performance computing to solve complex inferential problems that are intractable by traditional analytical ...

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 ...

Abstract: Computational Bayesian inference offers a flexible approach to answering important scientific questions regarding uncertainty. However, the Bayesian approach can reach its computational ...

Brazilian Journal of Probability and Statistics, Vol. 27, No. 1 (February 2013), pp. 1-19 (19 pages) We introduce a Bayesian analysis for beta generalized distributions and related exponentiated ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...