A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states

Yackulic, Charles B.; Dodrill, Michael J.; Dzul, Maria C.; Sanderlin, Jamie S.; Reid, Janice A.

doi:10.1002/eap.2112

Cited by 48 publications

(61 citation statements)

References 50 publications

(35 reference statements)

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“…Test sequence: 000000− / N animals: 0 Test sequence: 000000− / N animals: 197 The forward algorithm is a type of marginalisation that partly explains the better performance of the Stan version of the model. However, (Yackulic et al, 2020) also compared the speed of the marginalised versions of their model in different programmes and observed that Stan was orders of magnitude faster than JAGS.…”

Section: Discussionmentioning

confidence: 99%

“…showed that the marginalisation of the latent states considerably reduces the time needed to estimate the parameters of such models while returning the same estimates. We did not implement this approach in JAGS, although this would have been possible using the ones trick, as explained in the article by Yackulic et al (2020). The forward algorithm is a type of marginalisation that partly explains the better performance of the Stan version of the model.…”

Section: Predicted Probabilities Of Infectionmentioning

confidence: 99%

“…This was achieved by adapting a Stan implementation of the forward algorithm developed by others(Damiano et al, 2018). The Stan implementation is by comparison much faster and converges better, and should therefore be preferred.When estimated in either JAGS or BUGS, discrete latent state models such as HMMs are known to converge slowly; and the autocorrelation in the draws from the posterior distributions is usually high Yackulic et al (2020).…”

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confidence: 99%

“…However,Yackulic et al (2020) also compared the speed of the marginalised versions of their model in dierent programmes and observed that Stan was orders of magnitude faster than JAGS.In this model, the latent status is mostly dened by the prior distributions put on the dierent model parameters. In setting the prior distributions there are two issues: setting the distribution's central value (mean, median .…”

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confidence: 99%

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A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

Madouasse

Mercat

Roon

et al. 2020

Preprint

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For many infectious diseases of farm animals, there exist collective control programmes (CPs) that rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against infections by the bovine viral diarrhoea virus (BVDV) in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression in place of the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive in all herds on the current months. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model settings on parameter estimation was evaluated using the data. The main advantage of this model is its ability to predict a probability of being status positive on a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (PI) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree).

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Section: Discussionmentioning

confidence: 99%

Section: Predicted Probabilities Of Infectionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

Madouasse

Mercat

Roon

et al. 2020

Preprint

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“…2005, Kéry and Royle 2015, Yackulic et al. 2020). For each b = 1, … B removal period, our marginal likelihood is defined as

y_{r, i, j, t, b} \sim Poisson (λ_{r, i, j, t} \cdot π_{r, i, j, t, b})

where π r , i , j , t is equivalent to

π_{r, i, j, t}^{*}

but does not include the probability of failing to detect an individual that was truly present.…”

Section: Methodsmentioning

confidence: 99%

Trends in bird abundance differ among protected forests but not bird guilds

Doser

Weed

Zipkin

et al. 2021

Ecological Applications

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Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatiotemporal regions. We assess abundance trends for 106 bird species in a network of eight forested national parks located within the northeast United States from 2006 to 2019 using a novel hierarchical model. We develop a multispecies, multiregion, removal‐sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia National Park and Marsh‐Billings‐Rockefeller and Morristown National Historical Parks [NHP]) decreased in bird abundance across all species, while three parks (Saratoga NHP and Roosevelt‐Vanderbilt and Weir‐Farm National Historic Sites) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. By sharing information across both communities and parks, our novel hierarchical model enables uncertainty‐quantified estimates of abundance across multiple geographical (i.e., network, park) and taxonomic (i.e., community, guild, species) levels over a large spatiotemporal region. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition might have a broad and consistent effect on the entire bird community within a given park. Research should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors (e.g., pest outbreaks, climate change) is crucial for informed and lasting management.

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The Colorado River

et al. 2022

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A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 48 publications

References 50 publications

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

Trends in bird abundance differ among protected forests but not bird guilds

The Colorado River

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