Particle learning and smoothing
From MaRDI portal
Publication:903317
DOI10.1214/10-STS325zbMath1328.62541arXiv1011.1098MaRDI QIDQ903317
Hedibert Freitas Lopes, Michael S. Johannes, Nicholas G. Polson, Carlos Marinho Carvalho
Publication date: 5 January 2016
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1011.1098
smoothingstate space modelsparticle learningparameter learningmixture Kalman filtersequential inferencestate filtering
Inference from stochastic processes and prediction (62M20) Point estimation (62F10) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05) Sequential estimation (62L12)
Related Items
A new method for sequential learning of states and parameters for state-space models: the particle swarm learning optimization, State space mixed models for binary responses with scale mixture of normal distributions links, Sequential modeling, monitoring, and forecasting of streaming web traffic data, Sequential Bayesian analysis of multivariate count data, Sequential Monte Carlo smoothing with parameter estimation, Probabilistic logic programming for hybrid relational domains, Bayesian Computation in Dynamic Latent Factor Models, American Option Valuation with Particle Filters, Augmentation schemes for particle MCMC, Estimation of agent-based models using sequential Monte Carlo methods, Bayesian blinded sample size re-estimation, Model selection for time series of count data, A particle-learning-based approach to estimate the influence matrix of online social networks, Inference for differential equation models using relaxation via dynamical systems, Sequential Bayesian inference for static parameters in dynamic state space models, A Survey of Sequential Monte Carlo Methods for Economics and Finance, Online Bayesian learning for mixtures of spatial spline regressions with mixed effects, Efficient data augmentation techniques for some classes of state space models, Sequential estimation of temporally evolving latent space network models, Scalable inference for a full multivariate stochastic volatility model, Bayesian variable selection via particle stochastic search, A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions, News-Driven Uncertainty Fluctuations, Value at risk estimation under stochastic volatility models using adaptive PMCMC methods, An efficient sequential learning algorithm in regime-switching environments, Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning, Learning for infinitely divisible GARCH models in option pricing, Nested particle filters for online parameter estimation in discrete-time state-space Markov models, Bayesian statistics with a smile: a resampling-sampling perspective, Unnamed Item, Efficient learning via simulation: a marginalized resample-move approach, Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic, A family of multivariate non‐gaussian time series models, Dynamic quantile linear models: a Bayesian approach, Dynamic generalized extreme value modeling via particle filters, Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model, A NON‐GAUSSIAN FAMILY OF STATE‐SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD, Particle filters and Bayesian inference in financial econometrics, Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters, On some properties of Markov chain Monte Carlo simulation methods based on the particle filter, Inference for reaction networks using the linear noise approximation, Inference for the Hyperparameters of Structural Models Under Classical and Bayesian Perspectives: A Comparison Study, Motor unit number estimation via sequential Monte Carlo, Sequential parameter learning and filtering in structured autoregressive state-space models, Bayesian Conditional Density Filtering, Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models, Composable models for online Bayesian analysis of streaming data, Efficient \(\mathrm{SMC}^2\) schemes for stochastic kinetic models, A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving, Particle learning for Bayesian semi-parametric stochastic volatility model, Bayesian analysis of multivariate stochastic volatility with skew return distribution, Particle Learning for Fat-Tailed Distributions, Lookahead strategies for sequential Monte Carlo, On particle methods for parameter estimation in state-space models, Sequential Bayesian inference in hidden Markov stochastic kinetic models with application to detection and response to seasonal epidemics, Bayesian analysis of traffic flow on interstate I-55: the LWR model
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bayesian forecasting and dynamic models.
- Smoothing algorithms for state-space models
- Simulation-based sequential analysis of Markov switching stochastic volatility models
- Stability of the Gibbs sampler for Bayesian hierarchical models
- Particle learning for general mixtures
- Sequential Monte Carlo Methods in Practice
- Practical Filtering with Sequential Parameter Learning
- A sequential smoothing algorithm with linear computational cost
- On Gibbs sampling for state space models
- Mixture Kalman Filters
- Sequential Monte Carlo Methods for Dynamic Systems
- Filtering via Simulation: Auxiliary Particle Filters
- Monte Carlo Smoothing for Nonlinear Time Series