Results 41 to 50 of about 132,359 (288)
MCMC methods for restoration of nonlinearly distorted autoregressive signals [PDF]
Signal Processing, 2001 Publication in the conference proceedings of EUSIPCO, Rhodes, Greece ...
Troughton, P. T., Godsill, S. J.
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A hybrid adaptive MCMC algorithm in function spaces
, 2016 The preconditioned Crank-Nicolson (pCN) method is a Markov Chain Monte Carlo (MCMC) scheme, specifically designed to perform Bayesian inferences in function spaces. Unlike many standard MCMC algorithms, the pCN method can preserve the sampling efficiency
Hu, Zixi +3 more
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Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data [PDF]
, 2001 We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate ...
A. Abramovici +43 more
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Научно-технический вестник информационных технологий, механики и оптикиIn biology, information about interactions between the proteins or genes under study can be represented as a biological graph. A connected subgraph, whose vertices perform a common biological function, is called an active module.
D. A. Usoltsev +4 more
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Point Cloud Registration Based on MCMC-SA ICP Algorithm
IEEE Access, 2019 Point cloud registration is very important for workpiece positioning and error evaluation. Generally, the Iterative Closest Points (ICP) algorithm is always adopted as the first choice in fine registration, but requires a more appropriate initial ...
Haibo Liu +5 more
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Phylogenetic Stochastic Mapping without Matrix Exponentiation
, 2014 Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organisms carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov
Irvahn, Jan, Minin, Vladimir N.
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Fast Compression of MCMC Output
Entropy, 2021 We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates),
Nicolas Chopin, Gabriel Ducrocq
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Adaptive System Identification using Markov Chain Monte Carlo
, 2015 One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown
Anjum, Muhammad Ali Raza
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On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
, 2019 This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or ...
Han, Tian +4 more
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Sticky proposal densities for adaptive MCMC methods [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 Monte Carlo (MC) methods are commonly used in Bayesian signal processing to address complex inference problems. The performance of any MC scheme depends on the similarity between the proposal (chosen by the user) and the target (which depends on the problem).
Martino L., Casarin R., Luengo D.
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