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Bayesian Computation with R (Use R)
Bayesian Computation with R (Use R)
The book is a concise presentation of a wide range of Bayesian inferential problems and the computational methods to solve them. The detailed and thorough presentation style, with complete R code for the examples, makes it a welcome companion to a theoretical text on Bayesian inference.... Smart students of statistics will want to have both R and...
Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting.  This book presents many important techniques for building...
Constructing Measures: An Item Response Modeling Approach
Constructing Measures: An Item Response Modeling Approach
Constructing Measures introduces a way to understand the advantages and disadvantages of measurement instruments, how to use such instruments, and how to apply these methods to develop new instruments or adapt old ones. The book is organized around the steps taken while constructing an instrument. It opens with a summary of the...
Optimal Models and Methods with Fuzzy Quantities
Optimal Models and Methods with Fuzzy Quantities

The book contains ten chapters as follows, Prepare Knowledge, Regression and Self-regression Models with Fuzzy Coefficients; Regression and Self-regression Models with Fuzzy Variables, Fuzzy Input/output Model, Fuzzy Cluster Analysis and Fuzzy Recognition, Fuzzy Linear Programming, Fuzzy Geometric Programming, Fuzzy Relative Equation and Its...

Python for Data Science For Dummies (For Dummies (Computer/Tech))
Python for Data Science For Dummies (For Dummies (Computer/Tech))

Unleash the power of Python for your data analysis projectswith For Dummies!

Python is the preferred programming language for data scientistsand combines the best features of Matlab, Mathematica, and R intolibraries specific to data analysis and visualization. Pythonfor Data Science For Dummies shows you how to...

UNDERSTANDING STATISTICS
UNDERSTANDING STATISTICS
This is a book on the understanding of statistical concepts. If you have no knowledge, you will receive basic knowledge, without having to worry much about mathematics. And if you already know something about statistical methods, you will get a better understanding of the ideas behind them. All basic concepts are discussed in detail and...
SAS for Data Analysis: Intermediate Statistical Methods (Statistics and Computing)
SAS for Data Analysis: Intermediate Statistical Methods (Statistics and Computing)
This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are primarily graduate majors or advanced undergraduate students from a variety of disciplines. These students typically have...
Time Series Data Analysis Using EViews (Statistics in Practice)
Time Series Data Analysis Using EViews (Statistics in Practice)

Do you want to recognize the most suitable models for analysis of statistical data sets?

This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and...

Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so...
Financial Econometrics, Mathematics and Statistics: Theory, Method and Application
Financial Econometrics, Mathematics and Statistics: Theory, Method and Application

This rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset...

Numerical Issues in Statistical Computing for the Social Scientist (Wiley Series in Probability and Statistics)
Numerical Issues in Statistical Computing for the Social Scientist (Wiley Series in Probability and Statistics)
At last—a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical
...
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

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