IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions

Abstract

Bhattacharya (2021b) has introduced a novel methodology for generating iid realizations from any target distribution on the Euclidean space, irrespective of dimensionality. In this article, our purpose is two-fold. We first extend the method for obtaining iid realizations from general multimodal distributions, and illustrate with a mixture of two 50-dimensional normal distributions. Then we extend the iid sampling method for fixed-dimensional distributions to variable-dimensional situations and illustrate with a variable-dimensional normal mixture modeling of the well-known "acidity data", with further demonstration of the applicability of the iid sampling method developed for multimodal distributions.

An updated version after fixing some typos in the paper and code

References in corpus (4)

• Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm
• Nonstationary, Nonparametric, Nonseparable Bayesian Spatio-Temporal Modeling Using Kernel Convolution of Order Based Dependent Dirichlet Process
• Function Optimization with Posterior Gaussian Derivative Process
• Bayesian Levy-Dynamic Spatio-Temporal Process: Towards Big Data Analysis