Bayesian Modeling of Sequential Discoveries
Zito, A., Rigon, T., Ovaskainen, O., & Dunson, D. B. (2022). Bayesian Modeling of Sequential Discoveries. Journal of the American Statistical Association, Early online. https://doi.org/10.1080/01621459.2022.2060835
Published inJournal of the American Statistical Association
© 2022 American Statistical Association
We aim at modelling the appearance of distinct tags in a sequence of labelled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarised via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modelling by directly specifying the probability of a new discovery, therefore allowing for flexible specifications. The asymptotic behavior and finite sample properties of such an approach are extensively studied. Interestingly, our enlarged class of sequential processes includes highly tractable special cases. We present a subclass of models characterized by appealing theoretical and computational properties, including one that shares the same discovery probability with the Dirichlet process. Moreover, due to strong connections with logistic regression models, the latter subclass can naturally account for covariates. We finally test our proposal on both synthetic and real data, with special emphasis on a large fungal biodiversity study in Finland. ...
PublisherTaylor & Francis
Publication in research information system
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Related funder(s)European Commission
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Additional information about fundingThis project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 856506).
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