id: 06114140
dt: j
an: 06114140
au: Jasra, Ajay; Singh, Sumeetpal S.; Martin, James S.; McCoy, Emma
ti: Filtering via approximate Bayesian computation.
so: Stat. Comput. 22, No. 6, 1223-1237 (2012).
py: 2012
pu: Springer, Dordrecht
la: EN
cc: 
ut: hidden Markov model; bias; sequential Monte Carlo
ci: 
li: doi:10.1007/s11222-010-9185-0
ab: Summary: Approximate Bayesian computation (ABC) has become a popular
    technique to facilitate Bayesian inference from complex models. 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.
rv: