Titlebook: Bayesian Statistics from Methods to Models and Applications; Research from BAYSM Sylvia Frühwirth-Schnatter,Angela Bitto,Alexandra Confer

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2194-1009 thods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original r978-3-319-36234-2978-3-319-16238-6Series ISSN 2194-1009 Series E-ISSN 2194-1017

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A New Finite Approximation for the NGG Mixture Model: An Application to Density Estimationilt from the representation of the NGG process as a discrete measure, where the weights are obtained by normalization of points of a Poisson process larger than a threshold .. Consequently, the new process has an as surely finite number of location points. This process is then considered as the mixi

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A Subordinated Stochastic Process Modelction via subordination. This model is useful in many applications such as growth curves (children’s height, fish length, diameter of trees, etc.) and degradation processes (crack size, wheel degradation, laser light, etc.). One advantage of our approach is the ability to easily deal with data that

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Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Poste and expected-posterior prior, relying on the concept of random imaginary data, and provides a consistent variable selection method which leads to parsimonious selection. In this paper, the PCEP methodology is extended to generalized linear models (GLMs). We define the PCEP prior in the GLM setting,

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Application of Interweaving in DLMs to an Exchange and Specialization ExperimentWong, J. Am. Stat. Assoc. 82(398):528–540, 1987) is a commonly used approach, e.g. (Carter and Kohn, Biometrika 81(3):541–553, 1994) and (Frühwirth-Schnatter, J. Time Ser. Anal. 15(2):183–202, 1994) in dynamic linear models. Using two data augmentations, Yu and Meng (J. Comput. Graph. Stat. 20(3): 5

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Identifying the Infectious Period Distribution for Stochastic Epidemic Models Using the Posterior Prmic model. This method seeks to determine whether or not one can identify the infectious period distribution based only on a set of partially observed data using a posterior predictive distribution approach. Our criterion for assessing the model’s goodness of fit is based on the notion of Bayesian r