Sökning: "Markov chain Monte Carlo"

Visar resultat 1 - 5 av 69 avhandlingar innehållade orden Markov chain Monte Carlo.

  1. 1. Semi Markov chain Monte Carlo

    Författare :Håkan Ljung; Uppsala universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; Mathematics; Adaptive simulation; error-in-the-variables; Kullback-Leibler divergence; Markov chain simulation; Markov chain Monte Carlo; semi-regenerative; MATEMATIK; MATHEMATICS; MATEMATIK; matematisk statistik; Mathematical Statistics;

    Sammanfattning : The first paper introduces a new simulation technique, called semi Markov chain Monte Carlo, suitable for estimating the expectation of a fixed function over a distribution π, Eπf(χ). Given a Markov chain with stationary distribution p, for example a Markov chain corresponding to a Markov chain Monte Carlo algorithm, an embedded Markov renewal process is used to divide the trajectory into different parts. LÄS MER

  2. 2. Financial Applications of Markov Chain Monte Carlo Methods

    Författare :Andreas Graflund; Nationalekonomiska institutionen; []
    Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Finansiering; Financial science; ekonomiska system; ekonomisk politik; ekonomisk teori; Nationalekonomi; economic policy; economic systems; economic theory; Economics; econometrics; Mean Reversion; Diversification; Real Estate Stocks; Markov Chain Monte Carlo Methods; Stock Markets; ekonometri;

    Sammanfattning : This thesis consists of four empirical studies on financial economics. The first chapter contains a short summary of the thesis. LÄS MER

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

    Författare :Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; 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;

    Sammanfattning : 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. LÄS MER

  4. 4. Rare-event simulation with Markov chain Monte Carlo

    Författare :Thorbjörn Gudmundsson; Henrik Hult; Ad Ridder; KTH; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics;

    Sammanfattning : Stochastic simulation is a popular method for computing probabilities or expecta- tions where analytical answers are difficult to derive. It is well known that standard methods of simulation are inefficient for computing rare-event probabilities and there- fore more advanced methods are needed to those problems. LÄS MER

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

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

    Sammanfattning : 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. LÄS MER