Has anyone used the Think Stats book? Is it a good intro to stats?
Definitely good book on stats. Hands on, engaging and (horrors!) interesting ;-)
Think Bayes - Bayesian Statistics Made Simple
11–20 of 48 posts
Re: Think Bayes - Bayesian Statistics Made Simple
#12I'm really interested in knowing the prereqs I should have before picking up a book like this. Coming from a weak math background I find these books highly appealing but mildly intimidating. Also, could someone advise me on the preferred order of tackling the following Books? 1. Think Bayes 2. Think Stats 3. Programming Collective Intelligence by T.Segaran
3, 1, 2.
3: Get (back) into the swing of thinking about mathematics and algorithms.
1: Bayesian statistics is a principled, coherent, consistent, intuitive, complete framework for reasoning about uncertainty. A good foundation.
2. Traditional statistics is more random and ad-hoc, but can be more practical than Bayesian methods. (Bayesian models are well-motivated, but it can be impractical to compute exact answers and you'll have to switch to approximation techniques, some of which are simple/universal/slow, and others get fairly complex.)
Re: Think Bayes - Bayesian Statistics Made Simple
#13I found Udacity's class (CS373, ST101) and the 2011 ai class also explained bayesian very well by Sebastian Thrun.
Re: Think Bayes - Bayesian Statistics Made Simple
#14So, I'm going to counter here and say I don't find this to be a good intro. I started reading and had not heard of the "Girl named Florida" problem and then went to the linked to blog post http://allendowney.blogspot.com/2011/11/girl-named-florida-s... The way he explains it I found to be confusing and counter-intuitive. I've taken basic stats in college, and learned some of the associated problems, though not this o…
I agree that his first example, "The Girl Named Florida" was a confusing example.
I feel pretty comfortable with Bayesian statistics, and I thought the other examples that I saw were pretty clear. But his very first example jumps you out to another webpage, and then he mixes it with "the red-haired problem". It was irritating.
His next example, "The Cookie Problem" is the classic intro-to-Bayes example, IMO.
Re: Think Bayes - Bayesian Statistics Made Simple
#15My problem with books like this is that they have almost no connection to why Bayesian statistics is successful: Bayesian statistics provides a unified recipe to tackle complex data analysis problems. Arguably the only known unified recipe. The Bayesian book I want should emphasize how Bayes is a recipe for studying complex problems and teach a broad range of model ingredients. Learning Bayesian statistics is about b…
E.T. Jaynes book, "Probability Theory: the Logic of Science" may come close to what you want. It emphasize that there are rules of thought, which lead to Bayesian statistics. As such, Bayesian statistics aren't just a recipe, but the law.
Now, I can only personally vouch for the first 2 chapters, as I haven't read the rest yet.
Re: Think Bayes - Bayesian Statistics Made Simple
#16I'm really interested in knowing the prereqs I should have before picking up a book like this. Coming from a weak math background I find these books highly appealing but mildly intimidating. Also, could someone advise me on the preferred order of tackling the following Books? 1. Think Bayes 2. Think Stats 3. Programming Collective Intelligence by T.Segaran
If you are strong in Python but weak in math, then I would also recommend #3, (Collective Intelligence, by Segaran)
Re: Think Bayes - Bayesian Statistics Made Simple
#17My problem with books like this is that they have almost no connection to why Bayesian statistics is successful: Bayesian statistics provides a unified recipe to tackle complex data analysis problems. Arguably the only known unified recipe. The Bayesian book I want should emphasize how Bayes is a recipe for studying complex problems and teach a broad range of model ingredients. Learning Bayesian statistics is about b…
E.T. Jaynes book, "Probability Theory: the Logic of Science" may come close to what you want. It emphasize that there are rules of thought, which lead to Bayesian statistics. As such, Bayesian statistics aren't just a recipe, but the law . Now, I can only personally vouch for the first 2 chapters, as I haven't read the rest yet.
The greatness of this book cannot be overstated
Re: Think Bayes - Bayesian Statistics Made Simple
#18Earlier quoted context omitted.
E.T. Jaynes book, "Probability Theory: the Logic of Science" may come close to what you want. It emphasize that there are rules of thought, which lead to Bayesian statistics. As such, Bayesian statistics aren't just a recipe, but the law . Now, I can only personally vouch for the first 2 chapters, as I haven't read the rest yet.
The greatness of this book cannot be overstated
Agree. It is unbelievable - one has to study it to believe it.
Re: Think Bayes - Bayesian Statistics Made Simple
#19Bayesian is cool because you can make arbitrarily complex models, and when you have the parameters estimated it is really easy to calculate all the cool things you want to.
Bayesian is not cool because estimating the parameters takes bloody ages on a supercomputer, unless you spend ages being really careful to specify your model.
Frequentist statistics is cool because it is a massive big bag of tricks to estimate all sorts of stuff, and pretty much all of the tricks are already in R.
Frequentist statistics is not so cool because calculating all the specific things you want to can be a pain in the ass.
Once either Quantum computers kick in or a better algorithm than MCMC for Bayesian is created, Bayesian will win.
There are some philosophical arguments about the objectivity of the prior in Bayesian statistics, but these wash out in a decision theoretic framework because of the subjectivity of the utility function at the other end of the process.
Also, less than 5% of people reporting p-values really know what a p-value is.
Re: Think Bayes - Bayesian Statistics Made Simple
#20So this is all very well and good, I've had about 5 intros to Bayesian Statistics. But those are a fair bit away from actually applying that knowledge in practice in software.
Let's say we have N different kinds of events with unknown probabilities and unknown dependence or independence between them. The naive approach to gathering data on the probability of event n occurring following an occurrence of event m would require O(N²) in space. Let's say N ~ 10⁹~10¹⁰. Storing that much data as a raw matrix isn't practical in most cases, so we have to find a more efficient data structure - in terms of both space and the operations we need to perform. (and taking into account characteristics of the storage medium, i.e. memory or disk or a combination) What happens if the probabilistic properties of the system change over time?
Are there any introductory books or other resources on modeling this kind of problem? Clearly this has been tackled before, but I'm having a hard time making the leap from theory to practice - and I don't mean import the data into R or SPSS or whatever and let that grind out a solution, but coming up with approximations when you have runtime and space constraints that make that approach impractical.