Variable Selection with Big Data based on Zero Norm and via Sequential Monte Carlo

2019 ◽  
Author(s):  
Jin-Chuan Duan
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Variable Selection ◽  
Sequential Monte Carlo ◽  
Zero Norm

Scalable Fully Bayesian Gaussian Process Modeling and Calibration with Adaptive Sequential Monte Carlo for Industrial Applications

2021 ◽  
pp. 1-11
Author(s):  
Piyush Pandita ◽  
Panagiotis Tsilifis ◽  
Sayan Ghosh ◽  
Liping Wang
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Gaussian Process ◽  
Industry 4.0 ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Industrial Applications ◽  
Computational Time ◽  
Mcmc Sampling ◽  
Gp Model

Abstract Gaussian Process (GP) regression or kriging has been extensively applied in the engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the contexts of multi-fidelity modeling, model calibration and design optimization. With the ongoing automation of manufacturing and industrial practices as a part of Industry 4.0, there has been greater need for advancing GP regression techniques to handle challenges such as high input dimensionality, data paucity or big data problems, these consist primarily of proposing efficient design of experiments, optimal data acquisition strategies, and other mathematical tricks. In this work, our attention is focused on the challenges of efficiently training a GP model, which, to the authors opinion, has attracted very little attention and is to-date, poorly addressed. The performance of widely used training approaches such as maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) sampling can deteriorate significantly in high dimensional and big data problems and can lead to cost deficient implementations of critical importance to many industrial applications. Here, we compare an Adaptive Sequential Monte Carlo (ASMC) sampling algorithm to classic MCMC sampling strategies and we demonstrate the effectiveness of our implementation on several mathematical problems and challenging industry applications of varying complexity. The computational time savings of our ASMC approach manifest in large-scale problems helping us to push the boundary of GP regression applicability and scalability in various domain of Industry 4.0, including but not limited to design automation, design engineering, predictive maintenance, and supply chain manufacturing.

scholarly journals Estimating the Parameters of Dynamical Systems from Big Data Using Sequential Monte Carlo Samplers

2018 ◽  
Author(s):  
Peter Green ◽  
Simon Maskell
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Dynamical Systems ◽  
Structural Dynamics ◽  
Sequential Monte Carlo ◽  
Measurement Data ◽  
Parameter Estimates ◽  
Computationally Efficient

In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

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