Search for dissertations about: "sequential Monte Carlo SMC"

Showing result 1 - 5 of 15 swedish dissertations containing the words sequential Monte Carlo SMC.

  2. 1. Machine learning using approximate inference : Variational and sequential Monte Carlo methods

    Author : Christian Andersson Naesseth; Thomas Schön; Fredrik Lindsten; Iain Murray; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Abstract : Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. READ MORE

  4. 2. Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages

    Author : Daniel Lundén; David Broman; Lawrence Murray; Joakim Jaldén; Sam Staton; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Probabilistic programming languages; Compilers; Static program analysis; Monte Carlo inference; Operational semantics; Probabilistiska programmeringsspråk; Kompilatorer; Statisk programanalys; Monte Carlo-inferens; Operationell semantik; Informations- och kommunikationsteknik; Information and Communication Technology;

    Abstract : Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. READ MORE

  5. 3. Accelerating Monte Carlo methods for Bayesian inference in dynamical models

    Author : Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Computational statistics; Monte Carlo; Markov chains; Particle filters; Machine learning; Bayesian optimisation; Approximate Bayesian Computations; Gaussian processes; Particle Metropolis-Hastings; Approximate inference; Pseudo-marginal methods;

    Abstract : Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. READ MORE

  6. 4. On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space Models

    Author : Jimmy Olsson; Matematisk statistik; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; actuarial mathematics; programming; operations research; Statistics; Matematik; Mathematics; state space models; smoothing; sequential Monte Carlo; particle filter; EM algorithm; maximum likelihood; consistency; Asymptotic normality; Statistik; operationsanalys; programmering; aktuariematematik;

    Abstract : This thesis is based on four papers (A-D) treating filtering, smoothing, and maximum likelihood (ML) estimation in general state space models using stochastic particle filters (also referred to as sequential Monte Carlo (SMC) methods). The aim of Paper A is to study the bias of Monte Carlo integration estimates produced by the so-called bootstrap particle filter. READ MORE

  7. 5. Particle filters and Markov chains for learning of dynamical systems

    Author : Fredrik Lindsten; Thomas B. Schön; Lennart Ljung; Fredrik Gustafsson; Arnaud Doucet; Linköpings universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Bayesian learning; System identification; Sequential Monte Carlo; Markov chain Monte Carlo; Particle MCMC; Particle filters; Particle smoothers;

    Abstract : Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods. READ MORE