Results 51 to 60 of about 151,127 (170)

Multilevel sequential Monte Carlo samplers [PDF]

Stochastic Processes and their Applications, 2017
In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a ...
Beskos, Alexandros, Jasra, Ajay, Law, Kody, Tempone, Raul, Zhou, Yan   +4 more
openaire   +6 more sources

Tensor-Based Predictor–Corrector Algorithm for Power Generation and Transmission Reliability Assessment with Sequential Monte Carlo Simulation

Energies
The reliability assesment of large power systems, particularly when considering both generation and transmission facilities, is a computationally demanding and complex problem. The sequential Monte Carlo simulation is arguably the most versatile approach
Erika Pequeno dos Santos, Beatriz Silveira Buss, Mauro Augusto da Rosa, Diego Issicaba   +3 more
doaj   +1 more source

A Simple Method for Testing Independence of High-Dimensional Random Vectors

Austrian Journal of Statistics, 2016
A simple, data-driven and computationally efficient procedure for testing independence of high-dimensional random vectors is proposed. The procedure is based on interpretation of testing goodness-of-fit as the classification problem, a special sequential
Gintautas Jakimauskas, Marijus Radavičius, Jurgis Sušinskas   +2 more
doaj   +1 more source

СOMPUTATIONAL COMPLEXITY ANALYSIS OF RECURRENT DATA PROCESSING ALGORITHMS IN OPTICAL COHERENCE TOMOGRAPHY [PDF]

Научно-технический вестник информационных технологий, механики и оптики, 2014
The paper deals with the basic principles of signals representation in optical coherence tomography with the usage of dynamic systems theory formalism.
Maxim A. Volynsky , Igor P. Gurov , Peter A. Ermolaev , Pavel S. Skakov   +3 more
doaj  

Anytime Monte Carlo

Data-Centric Engineering, 2021
Monte Carlo algorithms simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of ...
Lawrence M. Murray, Sumeetpal S. Singh, Anthony Lee   +2 more
doaj   +1 more source

Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

Risks, 2017
The main objective of this work is to develop a detailed step-by-step guide to the development and application of a new class of efficient Monte Carlo methods to solve practically important problems faced by insurers under the new solvency regulations ...
Gareth W. Peters, Rodrigo S. Targino, Mario V. Wüthrich   +2 more
doaj   +1 more source

Convergence of the SMC implementation of the PHD filter [PDF]

, 2005
The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem.
Adam M. Johansen, Sumeetpal S. Singh, Arnaud Doucet, Ba-Ngu Vo, N. Chopin, D. J. Daley, P. Moral Del, P. Moral Del, R. P. S. Mahler, C. P. Robert, A. N. Shiryaev   +10 more
core   +1 more source

Neural Adaptive Sequential Monte Carlo

CoRR, 2015
Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance sampling-based methods, performance is critically dependent on the proposal distribution: a bad proposal can lead to arbitrarily inaccurate ...
Gu, Shixiang, Ghahramani, Zoubin, Turner, Richard E   +2 more
openaire   +3 more sources

Population Monte Carlo algorithms

, 2001
We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms. In these algorithms, a set of ``walkers'' or ``particles'' is used as a representation of a high-dimensional vector.
Gordon, N., Salmond, D., and Ewing,, Handschin J.E. and Mayne, D.Q., Higuchi, T., John S., and Remler, E.A., Kitagawa, G., Kitagawa, G., Liu J.S., and Chen, R., Liu J.S., and Chen, R., Metropolis, N., and Ulam, S., Negele J.W., and Brommer, K.D., Negele, J.W., Rosenbluth, M.N., and Rosenbluth, A, Shaw, L.J., and Henley, C.L., Wall, F.T., and Erpenbeck, J.J., Yukito Iba   +14 more
core   +2 more sources

Independent Resampling Sequential Monte Carlo Algorithms

, 2016
Sequential Monte Carlo algorithms, or Particle Filters, are Bayesian filtering algorithms which propagate in time a discrete and random approximation of the a posteriori distribution of interest.
Desbouvries, François, Lamberti, Roland, Petetin, Yohan, Septier, François   +3 more
core   +3 more sources