I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture ...

This study is important, advancing our understanding of how humans adapt to uncertainty in dynamic environments by investigating the interplay between two types of uncertainty-volatility (systematic ...

The three-day international conference at Banaras Hindu University brought together leading experts in Bayesian statistics to ...

Abstract: This paper introduces a Bayesian framework for image inversion by deriving ... Carlo algorithm specifically tailored for sampling from the resulting posterior distribution, based on an ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

Our goal is to describe the workflow of such an analysis and to explain how to generate informative results such as ranking plots and treatment risk posterior distribution plots. The R code used to ...

In this work, we recover this well-known fact from a Bayesian perspective. Our work further suggests that the prior distribution of cause and mechanism parameters should factorize, since such a ...

Bayex is a lightweight Bayesian optimization library designed for efficiency and flexibility, leveraging the power of JAX for high-performance numerical computations. This library aims to provide an ...