Sökning: "Sequential Monte Carlo method"

Visar resultat 1 - 5 av 24 avhandlingar innehållade orden Sequential Monte Carlo method.

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

   Författare :Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
   Nyckelord :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;

   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

3. 2. Sequential Monte Carlo for inference in nonlinear state space models

   Författare :Johan Dahlin; Thomas Schön; Fredrik Lindsten; Adam M. Johansen; Linköpings universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

   Sammanfattning : Nonlinear state space models (SSMs) are a useful class of models to describe many different kinds of systems. Some examples of its applications are to model; the volatility in financial markets, the number of infected persons during an influenza epidemic and the annual number of major earthquakes around the world. LÄS MER

4. 3. On inference in partially observed Markov models using sequential Monte Carlo methods

   Författare :Jonas Ströjby; Matematisk statistik; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : This thesis concerns estimation in partially observed continuous and discrete time Markov models and focus on both inference about the conditional distribution of the unobserved process as well as parameter inference for the dynamics of the unobserved process. Paper A concerns calibration of advanced stock price models, in particular the Bates and NIG-CIR models, using options data observed through bid-ask spreads. LÄS MER

5. 4. Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks

   Författare :Svetlana Bizjajeva; Matematisk statistik; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; positioning; state-space models; SMCM; particle filtering;

   Sammanfattning : This thesis is based on 5 papers exploring the filtering problem in non-linear non-Gaussian state-space models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks. The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength measurements. LÄS MER

7. 5. Bayesian Sequential Inference for Dynamic Regression Models

   Författare :Parfait Munezero; Mattias Villani; Helga Wagner; Stockholms universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Bayesian sequential inference; Dynamic regression models; Particle filter; Online prediction; Particle smoothing; Linear Bayes; Statistics; statistik;

   Sammanfattning : Many processes evolve over time and statistical models need to be adaptive to change. This thesis proposes flexible models and statistical methods for inference about a data generating process that varies over time. The models considered are quite general dynamic predictive models with parameters linked to a set of covariates via link functions. LÄS MER