This book describes modern tools for data analysis. With the exception of the last chapter, it is concerned with "supervised" methods - those methods in which a sample of cases is available, including values of an outcome variable, and on which one
In Techniques of Model-Based Control, two leading experts bring together powerful advances in model-based control for chemical-process engineering. Coleman Brosilow and Babu Joseph focus on practical approaches designed to solve real-world problems,
Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fty years, t
Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. All major statistical techniques covered in beginning statistics classes are included: descr iptive
The basic layout of my notes originally was constrained to the ve option themes of IB: geometry, discrete mathematics, abstract algebra, series and ordinary dierential equations, and inferential statistics. However, I have since added a short chapte
大数据时代的统计学 This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – ind
Table of Contents: Week 1 : Getting things started by defining different study types Getting to know study types Observational and experimental studies Getting to Know Study Types: Case Series Case-control Studies Cross-sectional studies Cohort stud
Biostatistics by Example Using SAS Studio PDF Purpose SAS University Edition and its user interface, SAS Studio, have become very popular. SAS Studio can also be used with standard versions of SAS, perhaps as an alternative to SAS Enterprise Guide.
Bayesian Networks are an important area of research and application within the domain of Artificial Intelligence. This paper explores the nature and implications for Bayesian Networks beginning with an overview and comparison of inferential statisti
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Process Optimization is a very important subject in todays economic climate. It is essential to run your plant as efficiently
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Markov chain Monte Carlo is a stochastic simulation
technique that is very useful for computing inferential
quantities. It is often used in a Bayesian context, but
not restricted to a Bayesian setting.
We consider a Bayesian analysis of linear regression models that can account for skewed
error distributions with fat tails. The latter two features are often observed characteristics
of empirical data sets, and we will formally incorporate them in th
