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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics

models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering;

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Enhanced Bayesian Network Models for Spatial Time Series Prediction

models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering;

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Regression Models For Time Series Analysis

concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series;

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Spatial & Spatio Temporal Bayesian

spatial and spatio--temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the;

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Essential Bayesian Models

of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric;

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Statistical Methods for Spatial Data Analysis

generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random;

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Statistical Methods for Spatial Data Analysis

-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with;

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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical;

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Bayesian Analysis of Time Series

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use;

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Bayesian Analysis of Time Series

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use;

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Bringing Bayesian Models to Life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental;

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Bringing Bayesian Models to Life

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental;

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Likelihood & Bayesian Inference

methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model;

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Likelihood and Bayesian Inference

methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Hidden Markov Models for Time Series

>Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested;

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Bayesian Inference for Stochastic Processes

in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov;

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Bayesian Inference for Stochastic Processes

in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For;

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Data-Driven Computational Neuroscience

of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Hidden Markov Models for Time Series

presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for;

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Einde inhoud

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