Results 71 to 80 of about 1,412,360 (277)
Forest resampling for distributed sequential Monte Carlo [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2015 This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the effective sample size (ESS) is sufficient to guarantee ...
Anthony Lee, Nick Whiteley
openaire +4 more sources
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Online sequential Monte Carlo smoother for partially observed diffusion processes
EURASIP Journal on Advances in Signal Processing, 2018 This paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the ...
Pierre Gloaguen +2 more
doaj +1 more source
Lookahead Strategies for Sequential Monte Carlo
Statistical Science, 2013 Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state.
Ming Lin, Rong Chen, Jun S. Liu
openaire +5 more sources
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +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 +2 more
doaj +1 more source
Sequential Monte Carlo video text segmentation [PDF]
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2004 This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences. The algorithm approximates the posterior distribution of segmentation thresholds of video text by a set of weighted samples. After initialization the set of samples is recursively refined by random sampling under a temporal Bayesian framework.
Datong Chen, Jean-Marc Odobez
openaire +2 more sources
Advanced Engineering Materials, EarlyView.A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič +7 more
wiley +1 more source
A Novel Sequential Monte Carlo Algorithm for Parameter Estimation in Eco‐Hydrological Models
Water Resources ResearchBayesian inference offers a flexible framework for parameter estimation and uncertainty quantification in eco‐hydrological models. However, simultaneously achieving robust posterior exploration and high computational efficiency for multimodal, high ...
Cong Xu +4 more
doaj +1 more source
New Journal of Physics, 2017 A major challenge facing existing sequential Monte Carlo methods for parameter estimation in physics stems from the inability of existing approaches to robustly deal with experiments that have different mechanisms that yield the results with equivalent ...
Christopher Granade, Nathan Wiebe
doaj +1 more source