Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

2015/02/06 by Julien Stoehr, Nial Friel, Stoehr, Julien +2
Computer Science · Decision Sciences · Environmental Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Probabilistic and Robust Engineering Design #Soil Geostatistics and Mapping #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1502.01997

openalex publication_date 2015/02/06 · openalex created_date 2018/07/10 · openalex updated_date 2026/08/01

Abstract

Gibbs random fields play an important role in statistics, however, the\nresulting likelihood is typically unavailable due to an intractable normalizing\nconstant. Composite likelihoods offer a principled means to construct useful\napproximations. This paper provides a mean to calibrate the posterior\ndistribution resulting from using a composite likelihood and illustrate its\nperformance in several examples.\n

Citations

• Bayesian Inference from Composite Likelihoods, with an Application to\n Spatial Extremes
• An introduction to exponential random graph (p*) models for social networks

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