Results 11 to 20 of about 717 (30)
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo [PDF]
, 2017 This paper presents a detailed theoretical analysis of the Langevin Monte Carlo sampling algorithm recently introduced in Durmus et al. (Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau, 2016) when applied ...
Brosse, Nicolas +3 more
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Detecting structural breaks in seasonal time series by regularized optimization
, 2015 Real-world systems are often complex, dynamic, and nonlinear. Understanding the dynamics of a system from its observed time series is key to the prediction and control of the system's behavior.
Motter, Adilson E., Sun, Jie, Wang, Bing
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, 2017 Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the ...
Djurić, Petar M. +2 more
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Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [PDF]
, 2014 We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter (Formula presented.).
Hastie, DI, Liverani, S, Richardson, S
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, 2009 Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to
Andreas Ipsen +8 more
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A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments [PDF]
, 2018 We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms.
Filová, Lenka +2 more
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SMART: A statistical framework for optimal design matrix generation with application to fMRI [PDF]
, 2009 The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from ...
Baumgartner, Richard +5 more
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GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation [PDF]
, 2014 Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging.
Meeds, Edward, Welling, Max
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Bias–Variance and Breadth–Depth Tradeoffs in Respondent-Driven Sampling [PDF]
, 2013 Respondent-driven sampling (RDS) is a link-tracing network sampling strategy for collecting data from hard-to-reach populations, such as injection drug users or individuals at high risk of being infected with HIV.
Blitzstein, Joseph K. +1 more
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Chain ladder method: Bayesian bootstrap versus classical bootstrap
, 2009 The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology.
Peters, Gareth W. +2 more
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