Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Likelihood-free inference and approximate Bayesian computation for stochastic modelling

Nilsson, Oskar (2013) In Master's Theses in Mathematical Sciences 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
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3227-2013
ISSN
1404-6342
other publication id
2013:E51
language
English
id
4066642
date added to LUP
2013-09-30 11:43:22
date last changed
2024-10-18 17:02:52
@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}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Likelihood-free inference and approximate Bayesian computation for stochastic modelling}},
  year         = {{2013}},
}