Results 21 to 30 of about 717 (30)
Exact Enumeration and Sampling of Matrices with Specified Margins [PDF]
, 2011 We describe a dynamic programming algorithm for exact counting and exact uniform sampling of matrices with specified row and column sums. The algorithm runs in polynomial time when the column sums are bounded.
Harrison, Matthew T., Miller, Jeffrey W.
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Nonparametric Bayesian methods for one-dimensional diffusion models
, 2013 In this paper we review recently developed methods for nonparametric Bayesian inference for one-dimensional diffusion models.
van Zanten, Harry
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, 2011 We study the correlation of the occurrence of coronary heart disease (CHD) with the presence of the single-nucleotide polymorphism (SNP) at the -308 position of the tumor necrosis factor alpha (TNF-$\alpha$) gene.
Carvalho, C. Sofia +1 more
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A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
, 2018 State space models in which the system state is a finite set--called the multi-object state--have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering by using the full ...
Vo, Ba Ngu, Vo, Ba Tuong
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How to Integrate a Polynomial over a Simplex
, 2008 This paper settles the computational complexity of the problem of integrating a polynomial function f over a rational simplex. We prove that the problem is NP-hard for arbitrary polynomials via a generalization of a theorem of Motzkin and Straus.
Baldoni, Velleda +4 more
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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
, 2018 Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates ...
Broderick, Tamara +3 more
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Predictive Entropy Search for Bayesian optimization with unknown constraints [PDF]
, 2015 Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic.
Adams, RP +4 more
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ABC for Climate: Dealing with Expensive Simulators [PDF]
, 2018 Edwards, Neil +3 more
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27th Annual Computational Neuroscience Meeting (CNS*2018): Part Two. [PDF]
BMC Neurosci, 2018 europepmc +1 more source