Sequential Monte Carlo with Highly Informative Observations

DOI10.1137/15M1011214zbMath1327.65004arXiv1405.4081OpenAlexW2124333705MaRDI QIDQ3452532

Lawrence M. Murray, Pierre Del Moral

Publication date: 12 November 2015

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.4081

zbMATH Keywords

stochastic differential equationBrownian bridgeBayesian statisticsstate-space modelcontinuous timeparticle filterapproximate Bayesian computationdiffusion bridgepinned diffusion

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Numerical solutions to stochastic differential and integral equations (65C30) Stochastic particle methods (65C35)

Related Items (9)

Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models ⋮ Some contributions to sequential Monte Carlo methods for option pricing ⋮ A coherent structure approach for parameter estimation in Lagrangian data assimilation ⋮ Improved bridge constructs for stochastic differential equations ⋮ Smoothing With Couplings of Conditional Particle Filters ⋮ Bagged Filters for Partially Observed Interacting Systems ⋮ Alive SMC²: Bayesian model selection for low‐count time series models with intractable likelihoods ⋮ Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter ⋮ Bayesian inference for Markov jump processes with informative observations

Uses Software

• LibBi
• Bridge.jl

Cites Work

• Lookahead strategies for sequential Monte Carlo
• Infectious disease models with time-varying parameters and general nonlinear incidence rate
• On parameter estimation in population models. II: Multi-dimensional processes and transient dynamics
• An adaptive sequential Monte Carlo method for approximate Bayesian computation
• On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
• Simulation of forward-reverse stochastic representations for conditional diffusions
• Simulation of conditioned diffusion and application to parameter estimation
• Guided proposals for simulating multi-dimensional diffusion bridges
• Genealogical particle analysis of rare events
• On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm
• Solving Ordinary Differential Equations I
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Particle Filters for Partially Observed Diffusions
• Adaptive approximate Bayesian computation
• Likelihood Inference for Discretely Observed Nonlinear Diffusions
• Filtering via Simulation: Auxiliary Particle Filters
• Random-Weight Particle Filtering of Continuous Time Processes
• Blind Deconvolution via Sequential Imputations
• On Generating Monte Carlo Samples of Continuous Diffusion Bridges
• Sequential Monte Carlo without likelihoods
• On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler
• A new algorithm for optimization
• SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Sequential Monte Carlo with Highly Informative Observations