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Hyperdynamics Importance Sampling
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Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Generalized darting Monte Carlo
Pattern Recognition
Sparse Linear Identifiable Multivariate Modeling
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In All Likelihood, Deep Belief Is Not Enough
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Robust Gaussian Process Regression with a Student-t Likelihood
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Neural Computation
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Variational multinomial logit gaussian process
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Behavioral game theoretic models: a Bayesian framework for parameter analysis
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Sequential Monte Carlo on large binary sampling spaces
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Parametric annealing: A stochastic search method for human pose tracking
Pattern Recognition
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Computer Vision and Image Understanding
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ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Computers and Structures
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Computation of marginal likelihoods with data-dependent support for latent variables
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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Simulated annealing—moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions—has traditionally been used as an inexact method of handling isolated modes in Markov chain samplers. Here, it is shown how one can use the Markov chain transitions for such an annealing sequence to define an importance sampler. The Markov chain aspect allows this method to perform acceptably even for high-dimensional problems, where finding good importance sampling distributions would otherwise be very difficult, while the use of importance weights ensures that the estimates found converge to the correct values as the number of annealing runs increases. This annealed importance sampling procedure resembles the second half of the previously-studied tempered transitions, and can be seen as a generalization of a recently-proposed variant of sequential importance sampling. It is also related to thermodynamic integration methods for estimating ratios of normalizing constants. Annealed importance sampling is most attractive when isolated modes are present, or when estimates of normalizing constants are required, but it may also be more generally useful, since its independent sampling allows one to bypass some of the problems of assessing convergence and autocorrelation in Markov chain samplers.