#312 in Science & math books
Use arrows to jump to the previous/next product

Reddit mentions of Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

Sentiment score: 4
Reddit mentions: 9

We found 9 Reddit mentions of Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Here are the top ones.

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
Buying options
View on Amazon.com
or
Specs:
Height9.3 Inches
Length7.8 Inches
Number of items1
Weight3.8360433588 Pounds
Width1.7 Inches

idea-bulb Interested in what Redditors like? Check out our Shuffle feature

Shuffle: random products popular on Reddit

Found 9 comments on Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan:

u/AllezCannes · 8 pointsr/statistics

I suggest you read John Kruschke's Doing Bayesian Data Analysis: http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884

It's a very approachable read. I myself have very little math background, but you will learn all you need. It is a large book though.

u/SupportVectorMachine · 5 pointsr/statistics

A very user-friendly treatment that hits every criterion you mention is John Kruschke's Doing Bayesian Data Analysis, Second Edition.

u/marmle · 4 pointsr/statistics

The short version is that in a bayesian model your likelihood is how you're choosing to model the data, aka P(x|\theta) encodes how you think your data was generated. If you think your data comes from a binomial, e.g. you have something representing a series of success/failure trials like coin flips, you'd model your data with a binomial likelihood. There's no right or wrong way to choose the likelihood, it's entirely based on how you, the statistician, thinks the data should be modeled. The prior, P(\theta), is just a way to specify what you think \theta might be beforehand, e.g. if you have no clue in the binomial example what your rate of success might be you put a uniform prior over the unit interval. Then, assuming you understand bayes theorem, we find that we can estimate the parameter \theta given the data by calculating P(\theta|x)=P(x|\theta)P(\theta)/P(x) . That is the entire bayesian model in a nutshell. The problem, and where mcmc comes in, is that given real data, the way to calculate P(x) is usually intractable, as it amounts to integrating or summing over P(x|\theta)P(\theta), which isn't easy when you have multiple data points (since P(x|\theta) becomes \prod_{i} P(x_i|\theta) ). You use mcmc (and other approximate inference methods) to get around calculating P(x) exactly. I'm not sure where you've learned bayesian stats from before, but I've heard good things , for gaining intuition (which it seems is what you need), about Statistical Rethinking (https://www.amazon.com/Statistical-Rethinking-Bayesian-Examples-Chapman/dp/1482253445), the authors website includes more resources including his lectures. Doing Bayesian data analysis (https://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=58357AYY9N1EZRG0WAMY) also seems to be another beginner friendly book.

u/jacobcvt12 · 3 pointsr/rstats

Both JAGS and BUGS use the same language and can perform very similar operations. JAGS is more portable across operating systems, so for that reason, I would suggest JAGS (BUGS is generally limited to Windows). However, documentation/blog posts/forum posts (which exist in abundance!) for both languages will generally work for either tool. If you are looking for a textbook, Doing Bayesian Data Analysis provides a nice introduction to both bayesian statistics as well as JAGS.

Outside of JAGS/BUGS, there exists another similar language for performing Bayesian statistics called Stan (also described in the above book). Stan is newer, and often times will "run faster" than JAGS, however it does not directly support as many types of analyses.

My advice would be to learn JAGS while simultaneously learning the basics of Bayesian methods. Once you understand the basics of JAGS, try exploring Stan!

u/beaverteeth92 · 2 pointsr/statistics

The absolute best book I've found for someone with a frequentist background and undergraduate-level math skills is Doing Bayesian Data Analysis by John Kruschke. It's a fantastic book that goes into mathematical depth only when it needs to while also building your intuition.

The second edition is new and I'd recommend it over the first because of its improved code. It uses JAGS and STAN instead of Bugs, which is Windows-only now.

u/kiwipete · 2 pointsr/statistics

An intermediate resource between the Downey book and the Gelman book is Doing Bayesian Analysis. It's a bit more grounded in mathematics and theory than the Downey, but a little less mathy than the Gelman.

u/coffeecoffeecoffeee · 1 pointr/statistics

One way is picking a distribution with a mode and a "concentration" around that mode that reflects what you have. John Kruschke does an amazing job at explaining how to pick Beta priors based off of that in Doing Bayesian Data Analysis (which, may I note, has the best cover of any statistics book I've ever read).

u/adcqds · 1 pointr/datascience

The pymc3 documentation is a good place to start if you enjoy reading through mini-tutorials: pymc3 docs

Also these books are pretty good, the first is a nice soft introduction to programming with pymc & bayesian methods, and the second is quite nice too, albeit targeted at R/STAN.

u/Sarcuss · 1 pointr/statistics

Many thanks for all the help efrique.

For Bayesian Data Analysis, I'm quite torn between Statistical Rethinking and [Doing Bayesian Data Analysis] (https://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884/ref=pd_bxgy_14_img_3?_encoding=UTF8&psc=1&refRID=QPN9262FN8TP9F2TC5BP).

Do you have any experience with one or the other?