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Parallel and Sequential Monte Carlo Methods with Applications [PDF]
Monte Carlo simulation methods are becoming increasingly important for solving difficult optimization problems. Monte Carlo methods are often used when it is infeasible to determine an exact result via a deterministic algorithm, such as with NP or #P problems.
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Sequential Monte Carlo methods under model uncertainty
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive ...
Iñigo Urteaga +2 more
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An Introduction to Sequential Monte Carlo Methods
2001Many real-world data analysis tasks involve estimating unknown quantities from some given observations. In most of these applications, prior knowledge about the phenomenon being modelled is available. This knowledge allows us to formulate Bayesian models, that is prior distributions for the unknown quantities and likelihood functions relating these ...
Arnaud Doucet +2 more
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Sequential Monte Carlo Methods in Practice
2001Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many ...
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Rates of Convergence for Sequential Monte Carlo Optimization Methods
SIAM Journal on Control and Optimization, 1978Sequential Monte Carlo methods of the stochastic approximation (SA) type, with and without constraints, are discussed. The rates of convergence are derived, and the quantities upon which the rates depend, are discussed. Let $\{ {X_n } \}$ denote the SA sequence and define $U_n = (n + 1)^\beta X_n $ for a suitable $\beta > 0$.
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Sequential Monte Carlo Methods for Neural Networks
2001Many problems, arising in science and engineering, require the estimation of nonlinear, time-varying functions that map a set of input signals to a corresponding set of output signals. Some examples include: finding the relation between an input pressure signal and the movement of a pneumatic control valve; using past observations in a time series to ...
N. Freitas +4 more
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General principles in sequential Monte Carlo methods
2003Abstract Simulation-based filters, such as the condensation algorithm and the Bayesian bootstrap or sampling importance resampling filter, aim to represent the joint posterior distribution of the unknown parameters and state variables in a dynamical statistical model by a discrete system of particles that evolves and adapts ...
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Sequential Monte Carlo Methods and Their Applications
2018Master of Applied Science (MASc)
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