When ecological individual heterogeneity models and large data collide: An importance sampling approach

King, Ruth; Sarzo, Blanca; Elvira, Víctor

doi:10.1214/23-aoas1753

Ann. Appl. Stat.

2023

DOI: 10.1214/23-aoas1753

|View full text |Cite

When ecological individual heterogeneity models and large data collide: An importance sampling approach

Ruth King,

Blanca Sarzo,

Víctor Elvira

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A point mass proposal method for Bayesian state-space model fitting

et al. 2023

Self Cite

View full text Add to dashboard Cite

State-space models (SSMs) are commonly used to model time series data where the observations depend on an unobserved latent process. However, inference on the model parameters of an SSM can be challenging, especially when the likelihood of the data given the parameters is not available in closed-form. One approach is to jointly sample the latent states and model parameters via Markov chain Monte Carlo (MCMC) and/or sequential Monte Carlo approximation. These methods can be inefficient, mixing poorly when there are many highly correlated latent states or parameters, or when there is a high rate of sample impoverishment in the sequential Monte Carlo approximations. We propose a novel block proposal distribution for Metropolis-within-Gibbs sampling on the joint latent state and parameter space. The proposal distribution is informed by a deterministic hidden Markov model (HMM), defined such that the usual theoretical guarantees of MCMC algorithms apply. We discuss how the HMMs are constructed, the generality of the approach arising from the tuning parameters, and how these tuning parameters can be chosen efficiently in practice. We demonstrate that the proposed algorithm using HMM approximations provides an efficient alternative method for fitting state-space models, even for those that exhibit near-chaotic behavior.

show abstract

A point mass proposal method for Bayesian state-space model fitting

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

When ecological individual heterogeneity models and large data collide: An importance sampling approach

Cited by 1 publication

References 33 publications

A point mass proposal method for Bayesian state-space model fitting

A point mass proposal method for Bayesian state-space model fitting

Contact Info

Product

Resources

About