Sense is a next-generation platform for data analysis, statistical modeling, and business analytics. We're building amazing technology and need help at all parts of the stack.
We're a tiny company of three. You will be a core team member building amazing technology in a fast paced, drama free, intellectually stimulating, environment. Competitive salary and equity.
= Lead Full Stack Web Developer =
* Experience building highly interactive, client-side, web applications (Backbone/AngularJS/Ember/etc).
* Deep knowledge of JavaScript / NodeJS.
* Experience building large systems on AWS.
* Highly productive and independent.
= Lead UI Designer =
* Fluent in Adobe Creative Suite.
* Pixel perfect design for print, mobile, and web UI.
* Ability to lead entire UI design and branding effort.
* Knowledge of JavaScript/HTML/CSS a plus but not required.
* Interest in data visualization a plus.
= Senior Technical Developer =
* Deep knowledge of numerical and statistical computing and familiarity with existing tools R/Matlab/SAS/SPSS/Stata.
* Experience building big data systems.
* Fluent in C++.
* Knowledge of JavaScript/V8/NodeJS a plus.
* Love of Bayesian statistics and MCMC samplers a plus.
Both are correct but they target different things. The disagreement is around what is the target should be and the advantages and disadvantages of choosing these targets. Bayesians are interested in p(unknown|data) and frequentists are interested in p(data|unknown = H0). Inference can be framed either way but means different things.
Are there any situations where you want to use a frequentist procedure?
I've concluded that given a perfect, infinite-power MCMC simulator, I would always do a Gelman-style Bayesian analysis (with model falsification and improvement), but in practice, frequentist methods are computationally convenient.
Inference can be framed either way but means different things.
A Bayesian posterior P(H|D,M) is the probability that hypothesis H is true given data D and modelling assumptions M.
Sure, see my link above (http://stats.stackexchange.com/a/2287/1122). If you want to put an upper bound on the worst-case probability of making a mistake, you use a p-value. If you want to express the conditional probability of a particular hypothesis given the observation (and given a prior belief), you use a posterior probability. The Bayesians also can do silly things (see the cookie example with the inept Bayesian robots). In the end there is no free lunch.
The frequentist p-value is about H0, not (directly) the hypothesis you are testing. More specifically, it denotes the probability of rejecting H0, even though it's true.
With as fast as it's working I would be shocked if a chunk of it wasn't being done locally. I think the refinement may be server side (IE: looking at the words in context against popular search strings to see if it may have miss-interpreted a word in the search string).
They are creating decentralized mechanisms for free individuals to find individually and socially beneficial outcomes. The free market organized by the U.S. legal system is one such mechanism, but certainly not the only one.
My 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 becoming fluent in describing scientific problems in probabilistic language. This requires knowing how to express and compose traditional models and build new ones based on first principles.
An unfortunate reality is that you still need to know computational methods too, but that should change soon enough.
Yes, that's exactly what the objective of this book is! I am not using computation out of necessity, but rather because I think it provides leverage for understanding the concepts, and learning to (as you say) compose traditional models and build new ones.
As the book comes along, I am finding that many ideas that are hard to explain and understand mathematically can be very easy to express computationally, especially using discrete approximations to continuous distributions.
I'd recommend using as many real examples as possible. Things like forecasting, product recommendations, topic modeling, etc. While you can conceptually explain how Bayesian statistics is a unified recipe, it's incredibly hard to have this sink in with toy problems. This is especially true since many people using traditional tools are actually using advanced methods to solve real problems, so when they start reading about urns or doors it all comes across as rather academic. That's sad because the benefit of Bayesian coherency is mostly that it leads to a highly productive mode of practical data analysis.
Definitely shoot me an email at tristan@senseplatform.com if you're interested in the computational side of this area. At Sense (http://www.senseplatform.com), we're working on making applied Bayesian analysis as amazing as it should be.
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.
You could come work at Sense (http://www.senseplatform.com) or email me tristan@senseplatform.com. Not Google scale, but the same technological challenges without the legacy bagage.
I'm really not sure this is true. I'd like to see evidence of it because certainly the people I know who buy android do it for features. That's also how they evangelize. It seems to work. Apple has a great marketing strategy but that doesn't mean the best competitive response is to mimic it.
