Biostatistics with R is designed to mimic the interaction between theory and application in statistics. Most topics are motivated by real examples first, and after discussing a topic, the author shows how it can be applied to the problem that motiva
Key Features Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building
贝叶斯应用方面教程。 Bayesian analysis of data in the health，social and physical sciences has been greatly facilitated in the last decade by advances in computing power and improved scope for estimation via iterative sampling methods. Yet the Bayesian perspec
BUGS是Bayesian inference using gibbs sampling的缩写）和WinBUGS。BUGS软件最初于1989年由位于英国剑桥的生物统计学研究所（Biostatistics the Medical Research Council, Cambridge, United Kingdom）研制的，现在由这个研究所和位于伦敦的S.t Mary's皇家学院医学分院（the Imperial College School of Medicine）共同开发。BUGS的运行以M
Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the
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.
This series aims to capture new developments and summarize what is known
over the entire spectrum of mathematical and computational biology and
medicine. It seeks to encourage the integration of mathematical, statistical,
and computational methods in
本书详细地介绍了如何用R语言进行meta分析，讲解全面细致并附上代码Chapman Hall/CRC Biostatistics Series
Editor-in-Chief
Shein-Chung Chow, Ph D
Professe
Department of Biostatistics and Bioinformatics
Duke University School of Medicine
Durham north carolina
Series Editors
Byron Jones
software used in this text is Stata, version 10 (StataCorp, 2007). It was chosen for the breadth and depth of its statistical methods, for its ease of use, excellent graphics and excellent documentation. There are several other excellent packages av
(1)Assess whether there are differential expressions between two groups of each gene. (2)Use the Bonferroni method to correct for multiple comparisons in Problem (1). Which genes show statistically significant differential expression? (3)Use the FDR
This introduction to biostatistics and measurement is the first in a series of articles
designed to provide Radiology readers with a basic understanding of statistical
concepts. Although most readers of the radiology literature know that applicatio
Hadley Wickham大神的Advanced R，适合R语言进阶者使用。Advanced r
K20319 FMindd 1
8/25/1412:28PM
Chapman Hall/cRc
Ther series
Series editors
John m. chambers
Torsten hothorn
Department of statistics
Division of biostatistics
Stanford University
University of zurich
Understanding Clinical Data Analysis，Learning Statistical Principles from Published Clinical Research
Ton J Cleophas Aeilko h. zwinderman
Understanding
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Clinical data
Analysis
Learning Statistical Principles from Published
Clinical research
S
ringer
