Statistical Data Modeling and Machine Learning with Applications

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New Concepts in Imaging: Optical and Statistical Models

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Promoting Statistical Practice and Collaboration in Developing Countries

Subjects: “…Probability & statistics bicssc…”
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Multivariate Statistical Analysis in the Real and Complex Domains by Mathai, Arak M.

Subjects: “…Probability & statistics bicssc…”
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Statistical Foundations of Actuarial Learning and its Applications by Wüthrich, Mario V.

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Promoting Statistical Practice and Collaboration in Developing Countries

Subjects: “…Probability & statistics bicssc…”
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Multivariate Statistical Analysis in the Real and Complex Domains by Mathai, Arak M.

Subjects: “…Probability & statistics bicssc…”
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Statistical Foundations of Actuarial Learning and its Applications by Wüthrich, Mario V.

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Statistical Physics of Spin Glasses and Information Processing An Introduction by Nishimori, Hidetoshi

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A Statistical Inquiry Into the Nature and Treatment of Epilepsy by Bennett, Alexander Hughes, 1848-1901

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The immigration offices and statistics from 1857 to 1903 by Argentina. Ministerio de Agricultura

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Sanitary Statistics of Native Colonial Schools and Hospitals by Nightingale, Florence, 1820-1910

Subjects: “…Hospital buildings -- Sanitation -- Statistics…”
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EzyStatistics : Aplikasi Statistik Sains Sosial by Michael, Roseline, Anas Muhiddin, Fatihah, Naharu, Nasrah, Sarahintu, Mazalan, Matmali, Norfazillah, Willie D. Tamsin, Nowyannie

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Chapter 2 Exploring the Components of the Universe Through Higher-Order Weak Lensing Statistics Higher-Order Weak Lensing Statistics by Dupé, François-Xavier

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Chapter 2 Exploring the Components of the Universe Through Higher-Order Weak Lensing Statistics Higher-Order Weak Lensing Statistics by Dupé, François-Xavier

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Multivariate Statistical Machine Learning Methods for Genomic Prediction by Montesinos López, Osval Antonio, Montesinos López, Abelardo, Crossa, José

Table of Contents: “…Preface -- Chapter 1 -- General elements of genomic selection and statistical learning -- Chapter. 2 -- Preprocessing tools for data preparation -- Chapter. 3 -- Elements for building supervised statistical machine learning models -- Chapter. 4 -- Overfitting, model tuning and evaluation of prediction performance -- Chapter. 5 -- Linear Mixed Models -- Chapter. 6 -- Bayesian Genomic Linear Regression -- Chapter. 7 -- Bayesian and classical prediction models for categorical and count data -- Chapter. 8 -- Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- Chapter. 9 -- Support vector machines and support vector regression -- Chapter. 10 -- Fundamentals of artificial neural networks and deep learning -- Chapter. 11 -- Artificial neural networks and deep learning for genomic prediction of continuous outcomes -- Chapter. 12 -- Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes -- Chapter. 13 -- Convolutional neural networks -- Chapter. 14 -- Functional regression -- Chapter. 15 -- Random forest for genomic prediction.…”
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Statistical Data Modeling and Machine Learning with Applications II

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Condensed-Matter-Principia Based Information & Statistical Measures From Classical to Quantum

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Statistical and Computational Methods for Microbiome Multi-Omics Data

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European Sourcebook of Crime and Criminal Justice Statistics - 2021 Sixth Edition by Aebi, Marcelo F.

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