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| Author | : BPP Learning Media |
| Publisher | : BPP Publishing |
| Total Pages | : 308 |
| Release | : 2009-07-01 |
| Genre | : Hospitality industry |
| ISBN | : 9780751777956 |
BPP Learning Media is proud to be the official publisher for CTH. Our CTH Study Guides provide the perfect tailor-made learning resource for the CTH examinations and are also a useful source of reference and information for those planning a career in the hospitality and tourism industries.
| Author | : BPP Learning Media |
| Publisher | : BPP Learning Media |
| Total Pages | : 321 |
| Release | : 2009-07-01 |
| Genre | : Business & Economics |
| ISBN | : 0751794414 |
BPP Learning Media is proud to be the official publisher for CTH. Our CTH Study Guides provide the perfect tailor-made learning resource for the CTH examinations and are also a useful source of reference and information for those planning a career in the hospitality and tourism industries.
| Author | : BPP Learning Media |
| Publisher | : BPP Learning Media |
| Total Pages | : 177 |
| Release | : 2009-07-01 |
| Genre | : Business & Economics |
| ISBN | : 0751794422 |
BPP Learning Media is proud to be the official publisher for CTH. Our CTH Study Guides provide the perfect tailor-made learning resource for the CTH examinations and are also a useful source of reference and information for those planning a career in the hospitality and tourism industries.
| Author | : |
| Publisher | : DIANE Publishing |
| Total Pages | : 240 |
| Release | : 1997 |
| Genre | : Biochemical oxygen demand |
| ISBN | : 1428906096 |
| Author | : Jeremy Watt |
| Publisher | : Cambridge University Press |
| Total Pages | : 597 |
| Release | : 2020-01-09 |
| Genre | : Computers |
| ISBN | : 1108480721 |
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
| Author | : John Kruschke |
| Publisher | : Academic Press |
| Total Pages | : 673 |
| Release | : 2010-11-25 |
| Genre | : Mathematics |
| ISBN | : 0123814863 |
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS software - Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). - Coverage of experiment planning - R and BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment
| Author | : Terri Janke |
| Publisher | : WIPO |
| Total Pages | : 172 |
| Release | : 2003 |
| Genre | : Law |
| ISBN | : 9280511890 |
"The World Intellectual Property Organization (WIPO) published on Monday, March 15, 2004, a collection of practical case studies on the use of the intellectual property sytsem by indigenous communities of Australia. It was written for WIPO by Terri Janke, an Australian lawyer, and a descendant of the Meriam people of the Torres Strait Islands, Australia."--
| Author | : National Digital Information Infrastructure and Preservation Program (U.S.) |
| Publisher | : |
| Total Pages | : 436 |
| Release | : 2008 |
| Genre | : Law |
| ISBN | : |
"This study focuses on the copyright and related laws of Australia, the Netherlands, the United Kingdom and the United States and the impact of those laws on digital preservation of copyrighted works. It also addresses proposals for legislative reform and efforts to develop non-legislative solutions to the challenges that copyright law presents for digital preservation"--P. i.
| Author | : John Kruschke |
| Publisher | : Academic Press |
| Total Pages | : 772 |
| Release | : 2014-11-11 |
| Genre | : Mathematics |
| ISBN | : 0124059163 |
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
| Author | : Andrew Gelman |
| Publisher | : CRC Press |
| Total Pages | : 677 |
| Release | : 2013-11-01 |
| Genre | : Mathematics |
| ISBN | : 1439840954 |
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
