Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

Deterministic simulation models are used to understand environmental processes and guide policy development by decision makers. In order to make informed decisions, uncertainty about input and output ...

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 ...

This is a preview. Log in through your library . Abstract In this paper a finite-dimensional diffusion is observed in the presence of an additive Brownian motion. A large deviations result is obtained ...

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 ...