Search results for "Monte Carlo method"
showing 10 items of 1234 documents
A Review of Multiple Try MCMC algorithms for Signal Processing
2018
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of {\it a-posteriori} estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a t…
Adaptive independent sticky MCMC algorithms
2018
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively based on previously drawn samples. The algorithm's efficiency is ensured by a test that controls the evolution of the set of support points. This extra stage controls the computational cost and the converge…
The Recycling Gibbs sampler for efficient learning
2018
Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions. The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions. Since in the general case this is not possible, in order to speed up the convergence of the chain, it is required to generate auxiliary samples whose information is eventually disregarded. In this work, we show that these auxiliary sample…
Unbiased Estimators and Multilevel Monte Carlo
2018
Multilevel Monte Carlo (MLMC) and unbiased estimators recently proposed by McLeish (Monte Carlo Methods Appl., 2011) and Rhee and Glynn (Oper. Res., 2015) are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction…
Parsimonious adaptive rejection sampling
2017
Monte Carlo (MC) methods have become very popular in signal processing during the past decades. The adaptive rejection sampling (ARS) algorithms are well-known MC technique which draw efficiently independent samples from univariate target densities. The ARS schemes yield a sequence of proposal functions that converge toward the target, so that the probability of accepting a sample approaches one. However, sampling from the proposal pdf becomes more computationally demanding each time it is updated. We propose the Parsimonious Adaptive Rejection Sampling (PARS) method, where an efficient trade-off between acceptance rate and proposal complexity is obtained. Thus, the resulting algorithm is f…
A study on the degree of relationship between two individuals.
2000
The paper studies the likely degree of relationship between two individuals who could possibly be half sibs. The possible common ancestor was dead, which further complicated the problem. The model used was devised by Thompson [in Rao and Chakraborty (eds): Handbook of Statistics, North-Holland, Amsterdam, 1991] and establishes a correspondence between the possible degree of relationship and certain feasible probability distributions on the number of identical by descent genes. Two statistical approaches are considered: the classical one, in which the maximum likelihood estimation for the parameters of Thompson’s model are obtained, and the Bayesian one, in which the test of the hypothesis o…
Simultaneous measurement of the muon neutrino charged-current cross section on oxygen and carbon without pions in the final state at T2K
2020
Authors: K. Abe,56 N. Akhlaq,45 R. Akutsu,57 A. Ali,32 C. Alt,11 C. Andreopoulos,54,34 L. Anthony,21 M. Antonova,19 S. Aoki,31 A. Ariga,2 T. Arihara,59 Y. Asada,69 Y. Ashida,32 E. T. Atkin,21 Y. Awataguchi,59 S. Ban,32 M. Barbi,46 G. J. Barker,66 G. Barr,42 D. Barrow,42 M. Batkiewicz-Kwasniak,15 A. Beloshapkin,26 F. Bench,34 V. Berardi,22 L. Berns,58 S. Bhadra,70 S. Bienstock,53 S. Bolognesi,6 T. Bonus,68 B. Bourguille,18 S. B. Boyd,66 A. Bravar,13 D. Bravo Berguño,1 C. Bronner,56 S. Bron,13 A. Bubak,51 M. Buizza Avanzini ,10 T. Campbell,7 S. Cao,16 S. L. Cartwright,50 M. G. Catanesi,22 A. Cervera,19 D. Cherdack,17 N. Chikuma,55 G. Christodoulou,12 M. Cicerchia,24,† J. Coleman,34 G. Collazu…
New insights into electron spin dynamics in the presence of correlated noise
2011
The changes of the spin depolarization length in zinc-blende semiconductors when an external component of correlated noise is added to a static driving electric field are analyzed for different values of field strength, noise amplitude and correlation time. Electron dynamics is simulated by a Monte Carlo procedure which keeps into account all the possible scattering phenomena of the hot electrons in the medium and includes the evolution of spin polarization. Spin depolarization is studied by examinating the decay of the initial spin polarization of the conduction electrons through the D'yakonov-Perel process, the only relevant relaxation mechanism in III-V crystals. Our results show that, f…
Radiation leakage study for the Valencia applicators
2011
Abstract Introduction and purpose The Valencia applicators which are accessories of the microSelectron-HDR afterloader (Nucletron, Veenendaal, The Netherlands) are designed to treat skin lesions. These cup-shaped applicators are an alternative to superficial/orthovoltage x-ray treatment units. They limit the irradiation to the required area using tungsten-alloy shielding, and are equipped with a tungsten-alloy flattering filter allowing the treatment of skin tumors, the oral cavity, vaginal cuff, etc. The tungsten-alloy thickness to shield radiation is not the same in all parts of the applicators. This fact led us to question whether the leakage radiation differs depending on where it is me…
LARGE-SCALE SIMULATIONS IN CONDENSED MATTER PHYSICS —THE NEED FOR A TERAFLOP COMPUTER
1992
The introduction of vector processors {“supercomputers” with a performance in the range of 109 floating point operations (1 GFLOP) per second} has had an enormous impact on computational condensed matter physics. The possibility of a substantially enhanced performance by massively parallel processors (“teraflop” machines with 1012 floating point operations per second) will allow satisfactory treatment of a large range of important scientific problems which have to a great extent thus far escaped numerical resolution. The present paper describes only a few examples (out of a long list of interesting research problems!) for which the availability of “teraflops” will allow spectacular progres…