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Likelihood-free inference and approximate Bayesian computation for stochastic modelling

Nilsson, Oskar (2013) FMS820 20132
Mathematical Statistics
Abstract (Swedish)
With increasing model complexity, sampling from the posterior distribution in a Bayesian context
becomes challenging. The reason might be that the likelihood function is analytically unavailable or
computationally costly to evaluate. In this thesis a fairly new scheme called approximate Bayesian
computation is studied which, through simulations from the likelihood function, approximately
simulates from the posterior. This is done mainly in a likelihood-free Markov chain Monte Carlo
framework and several issues concerning the performance are addressed. Semi-automatic ABC,
producing near-sucient summary statistics, is applied to a hidden Markov model and the same
scheme is then used, together with a varying bandwidth, to make... (More)
With increasing model complexity, sampling from the posterior distribution in a Bayesian context
becomes challenging. The reason might be that the likelihood function is analytically unavailable or
computationally costly to evaluate. In this thesis a fairly new scheme called approximate Bayesian
computation is studied which, through simulations from the likelihood function, approximately
simulates from the posterior. This is done mainly in a likelihood-free Markov chain Monte Carlo
framework and several issues concerning the performance are addressed. Semi-automatic ABC,
producing near-sucient summary statistics, is applied to a hidden Markov model and the same
scheme is then used, together with a varying bandwidth, to make inference on a real data study
under a stochastic Lotka-Volterra model. (Less)
Please use this url to cite or link to this publication:
author
Nilsson, Oskar
supervisor
organization
course
FMS820 20132
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
4066642
date added to LUP
2013-09-30 11:43:22
date last changed
2013-09-30 11:43:22
@misc{4066642,
  abstract     = {With increasing model complexity, sampling from the posterior distribution in a Bayesian context
becomes challenging. The reason might be that the likelihood function is analytically unavailable or
computationally costly to evaluate. In this thesis a fairly new scheme called approximate Bayesian
computation is studied which, through simulations from the likelihood function, approximately
simulates from the posterior. This is done mainly in a likelihood-free Markov chain Monte Carlo
framework and several issues concerning the performance are addressed. Semi-automatic ABC,
producing near-sucient summary statistics, is applied to a hidden Markov model and the same
scheme is then used, together with a varying bandwidth, to make inference on a real data study
under a stochastic Lotka-Volterra model.},
  author       = {Nilsson, Oskar},
  language     = {eng},
  note         = {Student Paper},
  title        = {Likelihood-free inference and approximate Bayesian computation for stochastic modelling},
  year         = {2013},
}