Author: Publisher:
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Languages : en
Pages : 9
Book Description
A Model Independent Determination of the B → X_1tsγ Decay Rate
Author: A Model Independent Determination of the B → X8[gamma] Decay Rate
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Author: A Model Independent Determination of the B → Xsg Decay Rate
Author: A Model Independent Analysis of the Rare B Decay B → Xsl+l-
Author: S. FukaeA model independent determination of the inclusive semileptonic decay fraction of B-mesons
Author: Updates on the Complement System in Kidney Diseases
Author: Mihály JózsiPublisher: Frontiers Media SA
ISBN: 2832522270
Category : Medical
Languages : en
Pages : 172
Book Description
Interpretable Machine Learning
Author: Christoph MolnarPublisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320
Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Hadron Collider Physics 2005
Author: Mario CampanelliPublisher: Springer Science & Business Media
ISBN: 3540328416
Category : Science
Languages : en
Pages : 360
Book Description
This book gathers the proceedings of The Hadron Collider Physics Symposia (HCP) 2005, and reviews the state-of-the-art in the key physics directions of experimental hadron collider research. Topics include QCD physics, precision electroweak physics, c-, b-, and t-quark physics, physics beyond the Standard Model, and heavy ion physics. The present volume serves as a reference for everyone working in the field of accelerator-based high-energy physics.
Mixed Effects Models for Complex Data
Author: Lang WuPublisher: CRC Press
ISBN: 9781420074086
Category : Mathematics
Languages : en
Pages : 431
Book Description
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
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