Likelihood-free inference and approximate Bayesian computation for stochastic modelling
Author
Summary, in 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 inference on a real data study
under a stochastic Lotka-Volterra model.
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.
Department/s
Publishing year
2013
Language
English
Full text
Document type
Student publication for Master's degree (two years)
Topic
- Mathematics and Statistics
Supervisor
- Umberto Picchini