Ajay, J and James, M and Emma, M and S S, S (2010) Filtering via approximate Bayesian computation. Technical Report. Cambridge University Engineering Department, Cambridge, UK.Full text not available from this repository.
Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering for a Hidden Markov Model when the likelihood function is intractable. We use a sequential Monte Carlo (SMC) algorithm to both fit and sample from our ABC approximation of the target probability density. This approach is shown to, empirically, be more accurate w.r.t.~the original filter than competing methods. The theoretical bias of our method is investigated; it is shown that the bias goes to zero at the expense of increased computational effort. Our approach is illustrated on a constrained sequential lasso for portfolio allocation to 15 constituents of the FTSE 100 share index.
|Item Type:||Monograph (Technical Report)|
|Additional Information:||Contact email address = firstname.lastname@example.org CUED/F-INFENG/TR.641|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||28 Oct 2011 17:06|
|Last Modified:||29 Jul 2013 01:06|
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