Corpus ID: 237940201

IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions

@inproceedings{Bhattacharya2021IIDSF,
  title={IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions},
  author={Sourabh Bhattacharya},
  year={2021}
}
  • Sourabh Bhattacharya
  • Published 26 September 2021
  • Mathematics
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… Expand

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References

SHOWING 1-10 OF 11 REFERENCES
Transdimensional transformation based Markov chain Monte Carlo
In this article, we propose a novel and general dimension-hopping MCMC methodology that can update all the parameters as well as the number of parameters simultaneously using simple deterministicExpand
Nonstationary, Nonparametric, Nonseparable Bayesian Spatio-Temporal Modeling using Kernel Convolution of Order Based Dependent Dirichlet Process
In this article, using kernel convolution of order based dependent Dirichlet process (Griffin and Steel (2006)) we construct a nonstationary, nonseparable, nonparametric space-time process, which, asExpand
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspacesExpand
Gibbs Sampling Based Bayesian Analysis of Mixtures with Unknown Number of Components
For mixture models with unknown number of components, Bayesian approaches, as considered by Escobar and West (1995) and Richardson and Green (1997), are reconciled here through a simple GibbsExpand
VARIABLE TRANSFORMATION TO OBTAIN GEOMETRIC ERGODICITY IN THE RANDOM-WALK METROPOLIS ALGORITHM
A random-walk Metropolis sampler is geometrically ergodic if its equilibrium density is super-exponentially light and satisfies a curvature condition [Stochastic Process. Appl. 85 (2000) 341-361].Expand
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixedExpand
Exact sampling with coupled Markov chains and applications to statistical mechanics
TLDR
This work describes a simple variant of the Markov chain method that determines on its own when to stop, and that outputs samples in exact accordance with the desired distribution, and can sample from the Gibbs distributions associated with various statistical mechanics models. Expand
Bayesian Levy-Dynamic Spatio-Temporal Process: Towards Big Data Analysis
In this era of big data, all scientific disciplines are evolving fast to cope up with the enormity of the available information. So is statistics, the queen of science. Big data are particularlyExpand
On Bayesian "central clustering": Application to landscape classification of Western Ghats
TLDR
A new methodology for obtaining the global, as well as the local modes of the posterior distribution of clustering, along with the desired credible and "highest posterior density" regions in a nonparametric Bayesian framework is proposed. Expand
Supplement to "Markov Chain Monte Carlo Based on Deterministic Transformations"
This is a supplement to the article "Markov Chain Monte Carlo Based on Deterministic Transformations" available at this http URL
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