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Conditional particle filters with diffuse initial distributions (2021)


Karppinen, S., & Vihola, M. (2021). Conditional particle filters with diffuse initial distributions. Statistics and Computing, 31(3), Article 24. https://doi.org/10.1007/s11222-020-09975-1


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatKarppinen, Santeri; Vihola, Matti

ISBN0960-3174

Lehti tai sarjaStatistics and Computing

ISSN0960-3174

eISSN1573-1375

Julkaisuvuosi2021

Volyymi31

Lehden numero3

Artikkelinumero24

KustantajaSpringer

JulkaisumaaSaksa

Julkaisun kielienglanti

DOIhttps://doi.org/10.1007/s11222-020-09975-1

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/74498


Tiivistelmä

Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random walk type transitions which are reversible with respect to a uniform initial distribution (on some domain), and autoregressive kernels for Gaussian initial distributions. We propose to use online adaptations within the methods. In the case of random walk transition, our adaptations use the estimated covariance and acceptance rate adaptation, and we detail their theoretical validity. We tested our methods with a linear Gaussian random walk model, a stochastic volatility model, and a stochastic epidemic compartment model with time-varying transmission rate. The experimental findings demonstrate that our method works reliably with little user specification and can be substantially better mixing than a direct particle Gibbs algorithm that treats initial states as parameters.


YSO-asiasanatbayesilainen menetelmäMarkovin ketjutmatemaattiset menetelmättilastotiede

Vapaat asiasanatAdaptive Markov chain Monte Carlo; bayesian inference; compartment model; conditional particle filter; diffuse initialisation; Hidden Markov model; smoothing; state space model


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointiKyllä

Raportointivuosi2021

JUFO-taso2


Viimeisin päivitys 2024-03-04 klo 20:06