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Reddit mentions of Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Sentiment score: 8
Reddit mentions: 13

We found 13 Reddit mentions of Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science). Here are the top ones.

Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
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Found 13 comments on Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science):

u/MRItopMD · 11 pointsr/medicalschool

Sure! I have a lot of resources on this subject. Before I recommend it, let me very quickly explain why it is useful.

Bayes Rule basically means creating a new hypothesis or belief based on a novel event using prior hypothesis/data. So I am sure you can already see how useful it would be in medicine to think about. The Rule(or technically theorem) is in fact an entire field of statisitcs and basically is one of the core parts of probability theory.

Bayes Rule explains why you shouldn't trust sensitivity and specificity as much as you think. It would take too long to explain here but if you look up Bayes' Theorem on wikipedia one of the first examples is about how despite a drug having 99% sensitivity and specificity, even if a user tests positive for a drug, they are in fact more likely to not be taking the drug at all.

Ok, now book recommendations:

Basic: https://www.amazon.com/Bayes-Theorem-Examples-Introduction-Beginners-ebook/dp/B01LZ1T9IX/ref=sr_1_2?ie=UTF8&qid=1510402907&sr=8-2&keywords=bayesian+statistics

https://www.amazon.com/Bayes-Rule-Tutorial-Introduction-Bayesian/dp/0956372848/ref=sr_1_6?ie=UTF8&qid=1510402907&sr=8-6&keywords=bayesian+statistics

Intermediate/Advanced: Only read if you know calculus and linear algebra, otherwise not worth it. That said, these books are extremely good and are a thorough intro compared to the first ones.

https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/ref=sr_1_1?ie=UTF8&qid=1510402907&sr=8-1&keywords=bayesian+statistics

https://www.amazon.com/Introduction-Probability-Chapman-Statistical-Science/dp/1466575573/ref=sr_1_12?s=books&ie=UTF8&qid=1510403749&sr=1-12&keywords=probability

u/[deleted] · 10 pointsr/statistics

Books:

"Doing Bayesian Data Analysis" by Kruschke. The instruction is really clear and there are code examples, and a lot of the mainstays of NHST are given a Bayesian analogue, so that should have some relevance to you.

"Bayesian Data Analysis" by Gelman. This one is more rigorous (notice the obvious lack of puppies on the cover) but also very good.

Free stuff:

"Think Bayes" by our own resident Bayesian apostle, Allen Downey. This book introduces Bayesian stats from a computational perspective, meaning it lays out problems and solves them by writing Python code. Very easy to follow, free, and just a great resource.

Lecture: "Bayesian Statistics Made (As) Simple (As Possible)" again by Prof. Downey. He's a great teacher.

u/CrazyStatistician · 10 pointsr/statistics

Bayesian Data Analysis and Hoff are both well-respected. The first is a much bigger book with lots of applications, the latter is more of an introduction to the theory and methods.

u/siddboots · 9 pointsr/statistics

It is hard to provide a "comprehensive" view, because there's so much disperate material in so many different fields that draw upon probability theory.

Feller is an approachable classic that covers all of the main results in traditional probability theory. It certainly feels a little dated, but it is full of the deep central limit insights that are rarely explained in full in other texts. Feller is rigorous, but keeps applications at the center of the discussion, and doesn't dwell too much on the measure-theoretical / axiomatic side of things. If you are more interested in the modern mathematical theory of probability, try Probability with Martingales.

On the other hand, if you don't care at all about abstract mathematical insights, and just want to be able to use probabilty theory directly for every-day applications, then I would skip both of the above, and look into Bayesian probabilistic modelling. Try Gelman, et. al..

Of course, there's also machine learning. It draws on a lot of probability theory, but often teaches it in a very different way to a traditional probability class. For a start, there is much more emphasis on multivariate models, so linear algebra is much more central. (Bishop is a good text).

u/DiogenicOrder · 8 pointsr/statistics

How would you rather split beginner vs intermediate/advanced ?

My feeling was that Ben Lambert's book would be a good intro and that Bayesian Data Analysis would be a good next ?

u/de_shrike · 7 pointsr/india

It can't be helped, the price is almost the same for the first book on amazon.com and I assume a similar trend for the others. Hardcovers in general are more expensive due to the intrinsic higher cost of manufacturing. What you may have observed is that other books have lower cost Asian editions that make them more affordable for nations with smaller economies, but these research books do not serve such a niche as such.

What is interesting though is that state of the art Machine Learning is usually not in these books, and is simply being published in papers and blog posts as of late.

u/timshoaf · 5 pointsr/education

> Elementary Statistics
http://ftp.cats.com.jo/Stat/Statistics/Elementary%20Statistics%20%20-%20Picturing%20the%20World%20(gnv64).pdf

Presuming you mean this book, I am still at an absolute loss to understand how you think this doesn't somehow require algebra as a prerequisite.

All the manipulations about gaussian distributions, student t distributions, binomial distribution etc... or even the bit on regression, right there on page 502, how is that not algebra. It literally makes reference to the general form of a line in 2-space. Are they just expected to memorize those outright with no regard to their derivation?

How do you treat topics like expected value? Because it seems like right there on page 194 that they've given the general algebraic formula for discrete, real valued, random variable.

They seem to elide the treatment of continuous random variables. So I presume they won't even be going through the exercise of the mean of a Poisson.

All of that granted, this book still heavily relies on the ability to perform algebraic permutations. Right there on page 306 is the very z-score transform I explicitly mentioned earlier.

As far as where I teach, I don't, excepting the odd lecture to clients or coworkers. Typically, however, our domain does not fit prettily into the packaged up parameterized distributions of baccalaureate statistics. We deal in a lot of probabilistic graphical models, in manifold learning, in non-parametrics, etc.

The books I recommend to my audience (which is quite different than those who haven't a basic grasp on algebra) are:

u/schmook · 3 pointsr/brasil

Na verdade eu sou físico. Acho que é mais comum entre os físicos adotar uma perspectiva bayesiana do que entre os matemáticos ou mesmo os estatísticos. Talvez por causa da influência do Edwin T. Jayes, que era físico. Talvez por causa da conexão com teoria de informação e a tentadora conexão com termodinâmica e mecânica estatística.

O meu interesse pela perspectiva Bayesiana começou por conta do grupo de pesquisa onde fiz o doutorado. Meus orientador e meu co-orientador são fortemente bayesianos, e o irmão do meu orientador de doutorado é um pesquisador bastante conhecido das bases epistemológicas da teoria bayesiana (o físico uruguaio Ariel Caticha).

Tem vários livros bons sobre probabilidade bayesiana, depende muito do seu interesse.

O primeiro livro que eu li sobre o assunto foi justamente o do Jaynes - Probability Theory, the Logic of Science. Esse é um livro um pouco polêmico porque ele adota uma visão epistemológica bastante forte e argumenta de forma bastante agressiva a favor dela.

Uma visão um pouco alternativa, bastante conectada com teoria de informação e também fortemente epistemológica você pode encontrar no livro Lectures on Probability, Entropy and Statistical Physics do Ariel Caticha - (de graça aqui: https://arxiv.org/abs/0808.0012). Eu fui aluno de doutorado do irmão do Ariel, o Nestor Caticha. Ambos têm uma visão bastante fascinante de teoria de probabilidades e teoria da informação e das implicações delas para a física e a ciência em geral.

Esses livros são mais visões epistemológicas e teóricas, e bem menos úteis para aplicação. Se você se interessa por aplicação tem o famoso BDA3 - Bayesian Data Analysis, 3ª edição e também o Doing Bayesian Data Analysis do John Kruschke que tem exemplos em R.

Tem um livrinho bem introdutório também chamado Bayesian Methods for Hackers do Cam-Davidson Pylon (de graça aqui: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) que usa exemplos em python (pymc). É bem basicão para aprender aplicações de probabilidades bayesianas.

O livro All of Statistics do Larry Wasserman tem uma parte introdutória também de inferência bayesiana.

Se você em interesse por inteligência artificial um outro livro muito bacana é o do físico britânico (recentemente falecido) David Mackay - Information Theory, Inference, and Learning Algorithms (de graça aqui: http://www.inference.phy.cam.ac.uk/mackay/itila/). Esse livro foi meu primeiro contato com Aprendizado de Máquina e é bem bacana.

Outros livros bacanas de Aprendizado de Máquina que usam uma perspectiva bayesiana são Bayesian Reasoning and Machine Learning (David Barber) e o livro-texto que tem sido o mais usado para essa área que é o Machine Learning: a Probabilistic Perspective (Kevin Murphy).



u/gatherinfer · 2 pointsr/statistics

A lot of the recommendations in this thread are good, I'd like to add "Bayesian Data Analysis 3rd edition" by Gelman et al. Useful if you encounter Bayesian models, especially hierarchical/multilevel models.

u/DavidJayHarris · 2 pointsr/statistics

This is very similar to the analysis featured on the cover of Bayesian Data Analysis (third edition).

Here's a bigger picture of their decomposition into day-of-week effects, seasonal effects, long-term trends, holidays, etc.

A bit more here, and lots more in the book.