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Practical Markov Chain Monte Carlo
The case is made for basing all inference on one long run of the Markov chain and estimating the Monte Carlo error by standard nonparametric methods well-known in the time-series and operations research literature. Expand
Markov Chain Monte Carlo Maximum Likelihood
Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basicsExpand
Constrained Monte Carlo Maximum Likelihood for Dependent Data
Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm orExpand
Annealing Markov chain Monte Carlo with applications to ancestral inference
Abstract Markov chain Monte Carlo (MCMC; the Metropolis-Hastings algorithm) has been used for many statistical problems, including Bayesian inference, likelihood inference, and tests of significance.Expand
Likelihood inference for spatial point processes
  • C. Geyer
  • Computer Science
  • Stochastic Geometry
  • 10 June 2019
On the Convergence of Monte Carlo Maximum Likelihood Calculations
SUMMARY Monte Carlo maximum likelihood for normalized families of distributions can be used for an extremely broad class of models. Given any family { he: 0 E 0 } of non-negative integrableExpand
On the Asymptotics of Constrained $M$-Estimation
Limit theorems for an $M$-estimate constrained to lie in a closed subset of $\mathbb{R}^d$ are given under two different sets of regularity conditions. A consistent sequence of global optimizersExpand
Simulation Procedures and Likelihood Inference for Spatial Point Processes
An alternative algorithm to the usual birth-and-death procedure for simulating spatial point processes is introduced. The algorithm is used in a discussion of unconditional versus conditionalExpand
Maximum likelihood for interval censored data: Consistency and computation
SUMMARY Standard convex optimization techniques are applied to the analysis of interval censored data. These methods provide easily verifiable conditions for the self-consistent estimator proposed byExpand