Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A constrained EM algorithm for principal component analysis
Neural Computation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Stability of Stochastic Approximation under Verifiable Conditions
SIAM Journal on Control and Optimization
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Short communication: Grapham: Graphical models with adaptive random walk Metropolis algorithms
Computational Statistics & Data Analysis
Robust adaptive photon tracing using photon path visibility
ACM Transactions on Graphics (TOG)
A computational framework for empirical Bayes inference
Statistics and Computing
Estimating discrete Markov models from various incomplete data schemes
Computational Statistics & Data Analysis
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Statistics and Computing
Robust adaptive Metropolis algorithm with coerced acceptance rate
Statistics and Computing
Adaptive Equi-Energy Sampler: Convergence and Illustration
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
On spectral invariance of randomized hessian and covariance matrix adaptation schemes
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Sequential Monte Carlo on large binary sampling spaces
Statistics and Computing
On adaptive Metropolis---Hastings methods
Statistics and Computing
Bayesian tests on components of the compound symmetry covariance matrix
Statistics and Computing
Sequential Monte Carlo EM for multivariate probit models
Computational Statistics & Data Analysis
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We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied. This leads to guidelines concerning the design of correct algorithms. We then review criteria and the useful framework of stochastic approximation, which allows one to systematically optimise generally used criteria, but also analyse the properties of adaptive MCMC algorithms. We then propose a series of novel adaptive algorithms which prove to be robust and reliable in practice. These algorithms are applied to artificial and high dimensional scenarios, but also to the classic mine disaster dataset inference problem.