A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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
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-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
- Skaalautuvat menetelmät luotettavaan Bayes-päättelyyn (SCALEBAYES)
- Vihola, Matti
- Suomen Akatemia
OKM-raportointi: Kyllä
Raportointivuosi: 2021
JUFO-taso: 2