The Normal Kernel Coupler: An Adaptive Markov Chain Monte Carlo Method for Efficiently Sampling From Multi-Modal Distributions

Abstract

The Normal Kernel Coupler (NKC) is an adaptive Markov Chain Monte Carlo (MCMC) method which maintains a set of current state vectors. At each iteration one state vector is updated using a density estimate formed by applying a normal kernel to the full set of states. This sampler is ergodic (irreducible, Harris recurrent and aperiodic) for any continuous distribution on d-dimensioual Euclidean space. The NKC outperforms standard MCMC methods on a variety of unimodal and bimodal problems in low to moderate dimension. We illustrate the utility of the NKC by fitting a mixture model for genetic instability in cancer cells. This model which has two distinct and dissimilar modes is not well handled by standard MCMC methods. In contrast, the NKC efficiently samples from this model and yields results that are consistent with current scientific understanding.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2001
Accession Number
ADA459460

Entities

People

  • Gregory R. Warnes

Organizations

  • George Washington University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Computing-Related Activities
  • Data Science
  • Information Operations
  • Information Science
  • Interdisciplinary Science
  • Iterations
  • Markov Chains
  • Mathematical Analysis
  • Mathematics
  • Monte Carlo Method
  • Numerical Analysis
  • Sampling
  • Statistical Analysis
  • Statistical Sampling
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Biotechnology
  • Space