Calibrated path sampling and stepwise bridge sampling

@article{Tan2013CalibratedPS,
  title={Calibrated path sampling and stepwise bridge sampling},
  author={Zhiqiang Tan},
  journal={Journal of Statistical Planning and Inference},
  year={2013},
  volume={143},
  pages={675-690}
}
  • Z. Tan
  • Published 1 April 2013
  • Mathematics
  • Journal of Statistical Planning and Inference

Figures and Tables from this paper

Optimally Adjusted Mixture Sampling and Locally Weighted Histogram Analysis

TLDR
A self-adjusted mixture sampling method, which accommodates both adaptive serial tempering and a generalized Wang–Landau algorithm, and develops an offline method, locally weighted histogram analysis, for estimating free energies and expectations, using all the simulated data from multiple distributions by either self- adjusted mixture sampling or other sampling algorithms.

Properties of the bridge sampler with a focus on splitting the MCMC sample

TLDR
Insightful insights are offered of the application of a combination of these adaptive methods to improve the accuracy of bridge sampling estimates in Bayesian applications based on the preceding investigations, with an application to a practical example.

Warp Bridge Sampling: The Next Generation

TLDR
Warp-U transformations that aim to transform multimodal densities into unimodal ones without altering their normalizing constants are introduced, and it is proved that the overlap between and is guaranteed to be no less than the overlap with p and , in terms of any f-divergence.

Methods in Monte Carlo Computation, Astrophysical Data Analysis and Hypothesis Testing with Multiply-Imputed Data

In Chapter 2, we propose a Bayesian hierarchical model to study the distribution of the X-ray intensities of stellar sources. One novelty of the model is its use of a zero-inflated gamma distribution

References

SHOWING 1-10 OF 20 REFERENCES

A cluster‐sample approach for Monte Carlo integration using multiple samplers

A computational problem in many fields is to estimate simultaneously multiple integrals and expectations, assuming that the data are generated by some Monte Carlo algorithm. Consider two scenarios in

Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling

TLDR
It is shown that the acceptance ratio method and thermodynamic integration are natural generalizations of importance sampling, which is most familiar to statistical audiences.

On a Likelihood Approach for Monte Carlo Integration

The use of estimating equations has been a common approach for constructing Monte Carlo estimators. Recently, Kong et al. proposed a formulation of Monte Carlo integration as a statistical model,

A theory of statistical models for Monte Carlo integration

Summary. The task of estimating an integral by Monte Carlo methods is formulated as a statistical model using simulated observations as data. The difficulty in this exercise is that we ordinarily

Marginal Likelihood from the Gibbs Output

  • S. Chib
  • Computer Science, Mathematics
  • 1995
TLDR
This work exploits the fact that the marginal density can be expressed as the prior times the likelihood function over the posterior density, so that Bayes factors for model comparisons can be routinely computed as a by-product of the simulation.

Prediction and Inference for Truncated Spatial Data

TLDR
Monte Carlo methods for prediction and inference problems based on the law of a truncated Gaussian random field are considered, which provides a method for computing maximum likelihood estimates of likelihood function for such models.

ESTIMATION OF LARGE FAMILIES OF BAYES FACTORS FROM MARKOV CHAIN OUTPUT

TLDR
This work considers situations in Bayesian analysis where the prior is indexed by a hyperparameter taking on a continuum of values, and develops a method for efficiently computing estimates of the entire family of Bayes factors.

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

TLDR
Each algorithm used in the article targets an approximation to the true posterior due to the use of Markov chain Monte Carlo method to simulate from the latent graphical model, in lieu of being able to do this exactly, in general.

Batch means and spectral variance estimators in Markov chain Monte Carlo

Calculating a Monte Carlo standard error (MCSE) is an important step in the statistical analysis of the simulation output obtained from a Markov chain Monte Carlo experiment. An MCSE is usually based

Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation

TLDR
A two‐stage algorithm for computing maximum likelihood estimates for a class of spatial models that combines Markov chain Monte Carlo methods, and stochastic approximation methods such as the off‐line average and adaptive search direction is proposed.