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Elliptical slice sampling
TL;DR: In this article, a Markov chain Monte Carlo (MCMCMC) algorithm was proposed for performing inference in models with multivariate Gaussian priors. But it has no free parameters, and it works well for a variety of Gaussian process based models.
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Abstract: Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.
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Citations
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Bayesian Optimization with Unknown Constraints
TL;DR: This paper studies Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently.
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•Proceedings Article
Variational Heteroscedastic Gaussian Process Regression
Michalis K. Titsias,Miguel L zaro-gredilla +1 more
- 28 Jun 2011
TL;DR: This work presents a non-standard variational approximation that allows accurate inference in heteroscedastic GPs (i.e., under input-dependent noise conditions) and its effectiveness is illustrated on several synthetic and real datasets of diverse characteristics.
A survey of preference-based reinforcement learning methods
TL;DR: A unified framework for PbRL is provided that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity.
Nonparametric Bayesian models through probit stick-breaking processes.
Abel Rodriguez,David B. Dunson +1 more
TL;DR: A novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables, allowing a great variety of models while preserving computational simplicity.
•Proceedings Article
Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray,Ryan P. Adams +1 more
- 06 Dec 2010
TL;DR: In this article, a slice sampling approach is presented that requires little tuning while mixing well in both strong and weak data regimes, in which the covariance structure can be specified using unknown hyperparameters.
References
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
TL;DR: This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.
Markov Chains for Exploring Posterior Distributions
TL;DR: Several Markov chain methods are available for sampling from a posterior distribution as discussed by the authors, including Gibbs sampler and Metropolis algorithm, and several strategies for constructing hybrid algorithms, which can be used to guide the construction of more efficient algorithms.
Hybrid Monte Carlo
TL;DR: In this article, a hybrid (molecular dynamics/Langevin) algorithm is used to guide a Monte Carlo simulation of lattice field theory, which is especially efficient for quantum chromodynamics which contain fermionic degrees of freedom.
4K
Log Gaussian Cox Processes
TL;DR: Planar Cox processes directed by a log Gaussian intensity process are investigated in the univariate and multivariate cases and the appealing properties of such models are demonstrated theoretically as well as through data examples and simulations.
925
Assessing Approximate Inference for Binary Gaussian Process Classification
Malte Kuss,Carl Edward Rasmussen +1 more
TL;DR: This work reviews and compares Laplace's method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model, and presents a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling.