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Spatial Predictive Modeling with R

The book is designed to be very accessible, with a focus on methods, examples, and computing, and theoretical details kept to an absolute;

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Modeling Techniques in Predictive Analytics

model that data. You'll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that;

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Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using;

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Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using;

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Mastering Predictive Analytics with R -

* Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types * Packed with practical advice and;

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Hierarchical Modeling and Analysis for Spatial Data

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first;

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Applied Predictive Modeling

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data;

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Personalized Predictive Modeling in Type 1 Diabetes

techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii;

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Supervised Machine Learning for Text Analysis in R

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive;...

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Supervised Machine Learning for Text Analysis in R

How do preprocessing steps such as tokenization, stemming, and removing stop words affect predictive;...

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Bayesian Disease Mapping

predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping;

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Bayesian Disease Mapping

predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping;

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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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Spatial Microsimulation with R

allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical;

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Spatial Microsimulation with R

allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical;

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GIS and Archaeological Site Location Modeling

in new ways. A guide to spatial procedures used in archaeology, the book provides a comprehensive treatment of predictive modeling. It draws;

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GIS and Archaeological Site Location Modeling

in new ways. A guide to spatial procedures used in archaeology, the book provides a comprehensive treatment of predictive modeling. It draws;

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Predictive Modeling Applications in Actuarial Science

to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1;

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Feature Engineering and Selection

with R programs for reproducing the results.;

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Feature Engineering and Selection

with R programs for reproducing the results.;

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Modeling Infectious Diseases in Humans and Animals

, focusing on recent developments as well as more traditional approaches. Matt Keeling and Pejman Rohani move from modeling with simple differential;

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Introduction to Spatial Econometrics

methods, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The;

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Introduction to Spatial Econometrics

, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The;

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Spatial Modeling in GIS and R for Earth and Environmental Sciences

Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS;

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Predictive Soil Mapping with R

sciences. Predictive Soil Mapping with R is about understanding the main concepts behind soil mapping, mastering R packages that can be used to;

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Predictive Modeling Applications In Actu

is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples;

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Expert Trading Systems

With the proliferation of computer programs to predict market direction, professional traders and sophisticated individual investors have;

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