Background Citations. Methods Citations. Results Citations. Topics from this paper. Computer programming Metropolis Multilevel model Censoring statistics Feature selection Conceptualization information science Computer program. Citation Type. Has PDF. Publication Type. More Filters. Bayesian data analysis for newcomers. View 1 excerpt. Computational Bayesian Statistics. View 3 excerpts, cites methods. Highly Influenced. View 10 excerpts, cites background and methods.
How to become a Bayesian in eight easy steps: An annotated reading list. View 6 excerpts, cites background. Bayesian estimation in hierarchical models. View 6 excerpts, cites background and methods. Book Review: Bayesian Statistics for Beginners. A Step-by-Step Approach. Donovan and Ruth M. Bayesian … Expand.
Bayesian methods applied to the generalized matching law. View 2 excerpts, cites methods. An introduction to using Bayesian linear regression with clinical data. Perhaps most We had quite stringent desiderata for a textbook introducing importantly, the students, when asked for their opinion at the end Bayesian statistics. On a 7 point scale ranging from 1 for pathetic to i. Second, it should require little or no mathematical An additional strength of the book is its compelling demon- and programming background.
Third, it should have exercises, stration of one of the big advantages the Bayesian approach has preferably with a solution manual. Finally, it should have a compared to the frequentist one. Unlike the latter approach, which practical orientation, implying that user-friendly code should be critically relies on exact or asymptotic distributions for test statis- available, preferably in R which, unlike Matlab, is free and open tics, the Bayesian framework grants considerable flexibility in source.
We therefore fully agree with Smithson , who The book also addresses recent approaches, such as transdi- in his review praised the book as filling a major gap in introductory mensional MCMC to perform model comparison chapter This textbooks on Bayesian statistics. The aim of our review is to share approach requires users to think about model indicators as bi- our experiences of having used the book in the classroom to teach nary parameters with a distribution and pseudo-priors, which the students how to do Bayesian data analysis.
Despite their nov- we have in the classroom, and starts from scratch. The first elty for the students, Kruschke admirably succeeds in explaining part chapters 2—4 reviews some basic but necessary aspects of these concepts. These chapters analysis chapter The second part chapters 5—13 introduces the guidelines are extremely useful—not necessarily as a canon, but main concepts on which Bayesian statistics is built, by means definitely as a stimulating starting point. It is a pedagogically wise choice and an impressive more attention.
First, default or reference priors, such as the accomplishment that all concepts and techniques, including Jeffreys prior, the Berger—Bernardo prior or the g prior are largely MCMC, are presented in the context of such a simple setting.
The neglected. An in depth treatment of this complicated topic is third and final part of the book chapters 14—22 applies this freshly probably not needed for an introductory textbook, but providing gained knowledge to the Generalized Linear Model framework, a brief discussion of this important research area and directing including ANOVA and regression. This unifying framework is a interested readers to the appropriate references seems essential clear asset of the book because it allows the students to connect for any textbook on Bayesian statistics.
On the conclusions you drew in the light of the missing data? Even the one hand, there are researchers who attempt to solve their if you sidestep this problem by telling the students that they inferential problem by estimating model parameters; and on the do not have to worry about it for the moment due to the other hand there are those who aim to choose the best model limited time available etc.
Kruschke, belonging to the often the code from the book did not work because of missing first group, somewhat disregards model checking and model or unbalanced data.
Our — extremely unsatisfying — ad hoc comparison. For example, a posterior predictive check PPC is solution to this problem was to rearrange the data in such a only recommended as being optional in a Bayesian analysis.
Of way the missingness would not get in the way of making the course, one can have methodological worries about the PPC, code run. Convergence problems are one of the bottlenecks know that the model fits the data reasonably well the parameter of the widespread use of Bayesian statistics. Indeed, almost all estimates are not meaningful at all. Further, the ability of Bayesian of the students encountered some problems with convergence model comparison to balance goodness-of-fit with complexity is of the MCMC chains.
The book appropriately stresses the only briefly mentioned. Few readers will get this feature based importance of checking that the chains have converged, but on the book alone, which is unfortunate, since the automatic offers little advice on steps to take when they do not. We Just like Kruschke, we wanted the students to learn how have found, for example, advanced yet practical techniques to actually do a Bayesian analysis.
After their graduation, most such as parameter expansion or hierarchical centering to be students will need to be able to apply statistics rather than very helpful. The code that comes with the book is clean and valuable than deep theoretical insights. Hence, the focus in our works well for the purposes it has been written. However, course was less working out gritty theoretical details e. For in a Bayesian way. Thus, we set up our course to have as few instance, one student had a three-factorial design with only a theoretical lectures as possible, leaving room for plenty hands single observation per cell.
Although she eventually succeeded, on experience. In particular, the first two parts of the book were it took quite some time and effort to adapt the code to her covered in a few theoretical lectures. These concepts were further relatively modest needs. Informative priors. One extremely powerful feature of the worked on the exercises from the covered chapters. Bayesian method is the fact that it is at least conceptually After these sessions, the students were asked to conduct a easy to incorporate prior knowledge into the analyses by Bayesian data analysis themselves.
Unlike the data sets used in means of an informative prior. Some students got over their most other statistics courses, the data sets for this analysis were initial worries that informative priors corrupt the supposed not streamlined, trimmed and preprocessed for analysis.
Rather, objectivity of science and wanted to take advantage of this the students were expected to analyze data they gathered them- feature. Unfortunately, all non-toy applications in the book selves for their master thesis. They could use any of the methods use generic or mildly informed priors. Having no examples from the third part of the book, or a combination thereof. These to fall back on, the students struggled with how to add prior chapters were not covered in class.
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