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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 toimittajat: Karppinen, Santeri; Vihola, Matti

ISBN: 0960-3174

Lehti tai sarja: Statistics and Computing

ISSN: 0960-3174

eISSN: 1573-1375

Julkaisuvuosi: 2021

Volyymi: 31

Lehden numero: 3

Artikkelinumero: 24

Kustantaja: Springer

Julkaisumaa: Saksa

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/s11222-020-09975-1

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

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


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-asiasanat: bayesilainen menetelmä; Markovin ketjut; matemaattiset menetelmät; tilastotiede

Vapaat asiasanat: Adaptive 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-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 2

Viimeisin päivitys 2022-14-09 klo 12:45