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 ...

In genetic analysis, there are often competing explanations for the same data. Sophisticated mathematical models have been developed that can encapsulate these problems in terms of parameters that ...

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 ...

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 ...

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 ...

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From within the dark confines of the skull, the brain builds its own version of reality. By weaving together expectations and information gleaned from the senses, the brain creates a story about the ...