Skip to main content

Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics.

Ben Goodrich, in a Stan forums survey of Stan video lectures, points us to the following book, which introduces Bayes, HMC, and Stan:

If Ben Goodrich is recommending it, it’s bound to be good. Amazon reviewers seem to really like it, too. You may remember Ben Lambert as the one who finally worked out the bugs in our HMM code for Stan for animal movement models; I blogged about it a couple years ago and linked the forum discussions where it was being worked out.

The linked page has answers to the exercises and an associated Shiny app for exploring distributions. There are also videos for a course based on the book:

I haven’t seen a copy, but I am very curious about the section titled “Bob’s bees in a house”, as it’s an example I’ve used in courses. I didn’t come up with the analogy—I borrowed it from a physics presentation on equilibrium in gases or something like that I’d seen somewhere.

Does anyone know if the Kindle version of this book is readable? Living and working in NYC, I have very limited space for physical books.



from Statistical Modeling, Causal Inference, and Social Science https://ift.tt/2U2QvoF
via IFTTT

Comments

Popular posts from this blog

Explaining models with Triplot, part 1

[This article was first published on R in ResponsibleML on Medium , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Explaining models with triplot, part 1 tl;dr Explaining black box models built on correlated features may prove difficult and provide misleading results. R package triplot , part of the DrWhy.AI project, is aiming at facilitating the process of explaining the importance of the whole group of variables, thus solving the problem of correlated features. Calculating the importance of explanatory variables is one of the main tasks of explainable artificial intelligence (XAI). There are a lot of tools at our disposal that helps us with that, like Feature Importance or Shapley values, to name a few. All these methods calculate individual feature importance for each variable separately. The problem arises when features used ...

The con behind every wedding

With her marriage on the rocks, one writer struggles to reconcile her cynicism about happily-ever-after as her own children rush to tie the knot A lavish wedding, a couple in love; romance was in the air, as it should be when two people are getting married. But on the top table, the mothers of the happy pair were bonding over their imminent plans for … divorce. That story was told to me by the mother of the bride. The wedding in question was two summers ago: she is now divorced, and the bridegroom’s parents are separated. “We couldn’t but be aware of the crushing irony of the situation,” said my friend. “There we were, celebrating our children’s marriage, while plotting our own escapes from relationships that had long ago gone sour, and had probably been held together by our children. Now they were off to start their lives together, we could be off, too – on our own, or in search of new partners.” Continue reading... The Guardian http://ift.tt/2xZTguV October 07, 2017 at 09:00AM