Titlebook: Case Studies in Applied Bayesian Data Science; CIRM Jean-Morlet Cha Kerrie L. Mengersen,Pierre Pudlo,Christian P. Robe Book 2020 The Editor

Infant

Bayesian Modelling to Assist Inference on Health Outcomes in Occupational Health Surveillancemportant workplace structures and repeated measurements. There is a need to provide systematic analyses of medical data that incorporate the data structure. Although multilevel statistical models may account for features of OHS data, current applications in occupational health medicine are often not

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Bayesian Networks for Understanding Human-Wildlife Conflict in Conservationquires evidence-based understanding of the complex system of factors that motivate and facilitate it. However, for many affected species, data on this sensitive subject are too sparse for many statistical techniques. This study considers two iconic wild cats under threat in diverse locations and emp

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septicemia

Thresholds of Coral Cover That Support Coral Reef Biodiversityosystem engineers, reef-building corals, on the Great Barrier Reef. Our ability to understand how coral associated species, such as reef fishes, respond to coral loss can be impeded by uncertainty surrounding natural spatio-temporal variability of coral populations. To address this issue, we develop

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Thermal modelling of phase-change recording,als with Bayesian modeling and part B which presents real-world case studies. The last part of the chapter details the organization of the various events related to the Jean-Morlet Chair. It ends with the issues and research directions identified by the participants of the Conference on Bayesian Sta

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Theoretical aspects of phase-change alloys,nd insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a survey of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefi

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Conspiracy

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YA. V. PAVLENK,I.B. VAVILOVA,T. KOSTIUKof the human brain. State-of-the-art analyses such as linear mixed effects (LME) models and pairwise descriptive correlation networks are usually performed independently, providing an incomplete picture of the relationships between the biomarkers and network organisation. Furthermore, descriptive ne

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Astrophysics and Space Science Librarynditions. The classification of action potentials, or “spike sorting”, can be formulated as an unsupervised clustering problem, and latent variable models such as mixture models are often used. In this chapter, we compare the performance of two mixture-based approaches when applied to spike sorting: