Corpus ID: 237493782

Diagnostics for Monte Carlo Algorithms for Models with Intractable Normalizing Functions

@inproceedings{Kang2021DiagnosticsFM,
  title={Diagnostics for Monte Carlo Algorithms for Models with Intractable Normalizing Functions},
  author={Bokgyeong Kang and John Hughes and Murali Haran},
  year={2021}
}
Models with intractable normalizing functions have numerous applications ranging from network models to image analysis to spatial point processes. Because the normalizing constants are functions of the parameters of interest, standard Markov chain Monte Carlo cannot be used for Bayesian inference for these models. A number of algorithms have been developed for such models. Some have the posterior distribution as the asymptotic distribution. Other “asymptotically inexact” algorithms do not… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 38 REFERENCES
A Function Emulation Approach for Doubly Intractable Distributions
  • Jaewoo Park, M. Haran
  • Computer Science, Mathematics
  • Journal of Computational and Graphical Statistics
  • 2019
TLDR
A novel algorithm is proposed that provides computational gains over existing methods by replacing Monte Carlo approximations to the normalizing function with a Gaussian process-based approximation and is applicable more broadly to any likelihood function that is expensive to evaluate. Expand
Bayesian Inference in the Presence of Intractable Normalizing Functions
TLDR
This study compares and contrast the computational and statistical efficiency of these algorithms and discusses their theoretical bases, and provides practical recommendations for practitioners along with directions for future research for Markov chain Monte Carlo methodologists. Expand
Calibrated Approximate Bayesian Inference
TLDR
It is shown that the original approximate inference had poor coverage for these data and should not be trusted, by exploiting the symmetry of the coverage error under permutation of low level group labels and smoothing with Bayesian Additive Regression Trees. Expand
Bayesian computation for statistical models with intractable normalizing constants
This paper deals with some computational aspects in the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in theExpand
MCMC for Doubly-intractable Distributions
TLDR
This paper provides a generalization of M0ller et al. (2004) and a new MCMC algorithm, which obtains better acceptance probabilities for the same amount of exact sampling, and removes the need to estimate model parameters before sampling begins. Expand
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
A large number of statistical models are "doubly-intractable": the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal likelihood (modelExpand
Measuring Sample Quality with Kernels
TLDR
A theory of weak convergence for K SDs based on Stein's method is developed, it is demonstrated that commonly used KSDs fail to detect non-convergence even for Gaussian targets, and it is shown that kernels with slowly decaying tails provably determine convergence for a large class of target distributions. Expand
An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants
Sampling from the posterior distribution for a model whose normalizing constant is intractable is a long-standing problem in statistical research. We propose a new algorithm, adaptive auxiliaryExpand
A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
The problem of simulating from distributions with intractable normalizing constants has received much attention in recent literature. In this article, we propose an asymptotic algorithm, theExpand
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
SUMMARY A generalization of the sampling method introduced by Metropolis et al. (1953) is presented along with an exposition of the relevant theory, techniques of application and methods andExpand
...
1
2
3
4
...