Likelihood-free inference and approximate Bayesian computation for stochastic modelling
(2013) FMS820 20132Mathematical 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:
http://lup.lub.lu.se/student-papers/record/4066642
- author
- Nilsson, Oskar
- supervisor
- organization
- course
- FMS820 20132
- year
- 2013
- 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}}, }