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"Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes" by Xiaotian Zheng, Athanasios Kottas et al.

We develop a class of nearest-neighbor mixture models that provide direct, computationally efficient, probabilistic modeling for non-Gaussian geospatial data. The class is defined over a directed acyclic graph, which implies conditional independence in representing a multivariate distribution through factorization into a product of univariate conditionals, and is extended to a full spatial process. We model each conditional as a mixture of spatially varying transition kernels, with locally adaptive weights, for each one of a given number of nearest neighbors. The modeling framework emphasizes direct spatial modeling of non-Gaussian data, in contrast with approaches that introduce a spatial process for transformed data, or for functionals of the data probability distribution. We study model construction and properties analytically through specification of bivariate distributions that define the local transition kernels. This provides a general strategy for modeling different types of no ....

Mediterranean Sea , Oceans General , Bayesian Hierarchical Models , Markov Chain Monte Carlo , Spatial Statistics , Ail Dependence ,

"Statistical Deep Learning for Spatial and Spatiotemporal Data" by Christopher K. Wikle and Andrew Zammit-Mangion

Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational tec ....

Bayesian Hierarchical Models , Convolutional Neural Networks , Eep Gaussian Processes , Recurrent Neural Networks , Reinforcement Learning ,

Infectious disease modeling in a time of COVID-19 - PLOS ONE authors' perspectives

PLOS ONE,
PLOS Biology and
PLOS Computational Biology, on a variety of topics relevant to the modeling of infectious disease, such as disease spread, vaccination strategies and parameter estimation. As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We therefore invited a few authors featured in the Collection to give their perspectives on their research during this global pandemic. We caught up with Verrah Otiende (independent researcher, Pan African University Institute of Basic Sciences Technology and Innovation), Lauren White (USAID), Jess Liebig (CSIRO) and Johnny Whitman (The Ohio State University) to hear their reflections on this collection and the time that has passed. ....

Verrah Otiende , Lauren White , Jess Liebig , Johnny Whitman , Department Of Ecology , National Socio , University Of Maryland , Environmental Synthesis Center , Ohio State University , Technology Policy , University Of Minnesota , Technology Policy Fellowship , Infectious Disease , Pan African University Institute , Basic Sciences Technology , Bayesian Hierarchical Modeling , One Health , Applying Bayesian , Bayesian Hierarchical Models , Big Data , Named Entity Recognition , Natural Language Processing , Technology Policy Fellow , National Socio Environmental Synthesis Center , லாரன் வெள்ளை , ஜானி விட்மேன் ,

Algorithm streamlines targeted marketing efforts with unrivaled precision

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Monte Carlo , Journal Of Marketing Research , University Of Chicago Booth School Business , Anderson School Of Management , Leeds Assistant Professor , Marketing Rico Bumbaca , Booth School , Anderson School , Target Marketing , Distributed Markov Chain Monte Carlo , Bayesian Hierarchical Models , Marketing Research , மான்டே கார்லோ , இதழ் ஆஃப் சந்தைப்படுத்தல் ஆராய்ச்சி , பல்கலைக்கழகம் ஆஃப் சிகாகோ சாவடி பள்ளி வணிக , ஆண்டர்சன் பள்ளி ஆஃப் மேலாண்மை , லீட்ஸ் உதவியாளர் ப்ரொஃபெஸர் , சாவடி பள்ளி , ஆண்டர்சன் பள்ளி , இலக்கு சந்தைப்படுத்தல் , விநியோகிக்கப்பட்டது மார்க்கோவ் சங்கிலி மான்டே கார்லோ , சந்தைப்படுத்தல் ஆராய்ச்சி ,