Like Daniel Dvorkin has said Devore's book Probability and Statistics for Engineering and the Sciences is an excellent starting point.

Definitely learn to use R since its free you don't have to worry about paying licensing fees. It is also widely used (no matter what you here from SAS, Minitab, SPSS, etc).

Books I would recommend that I think fit his other suggestions are Bowerman/O'Connell Linear Statistical Models: An Applied Approach and Wackerly et al Mathematical Statistics with Applications

Devore talks about Bayes Rule as does Wackerly and Wackerly's last chapter talks about some Bayesian techniques, but these are merely primers for what is typical in a Bayesian course. So I recommend these two books as analogous with Devore's: Bolstad Introduction to Bayesian Statistics and to Wackerly's: Hoff A First Course in Bayesian Statistical Methods

Some things you need from mathematics are the ability to integrate, work with matrices and matrix operations, and algebraic manipulation. Familiarity with transformations and operators especially linear ones is useful since many procedures in statistics are linear operators. The highest levels of statistics will get even more math intense using mathematical results from areas like ODE/PDE, Galios Theory, or general Measure Theory.

The wikipedia's statistics articles are pretty good overall, but as Dvorkin noted some are more technical than what would be friendly to those that are new to statistics. When you feel that's the case try using the sources linked as citations in the article or google confusing parts and it is generally possible to find an explanation for almost any background level.

However if you can get through these texts you're background would be pretty strong.

Science is in the business of probably knowledge. So they really need to improve their probability and statistics knowledge.

It is not a shortcoming of statistics that other people, like various scientists who aren't statisticians, don't know how to use or properly interpret statistics. It is a shortcoming of their knowledge.

It is not a shortcoming of the Copenhagen interpretation of quantum mechanics or the Chicago school of economics if I don't understand or know how to correctly interpret their results. It is my shortcoming and fault for not knowing enough to connect the dots.

I do statistical research some of that is through interacting with researchers in the biosciences. Often when I go to talk to a researcher and ask them if they could use some statistical or mathematical or computational assistance with their research it has almost always been a fruitful starting point to long conversations and getting into the research. Now sometimes it was simply a matter of looking at their F-test results or ANOVA scores and telling them what it meant (like with a regression model relating proportions of certain characteristics between taxa), more useful interactions for me often mean working on new algorithms or estimators or working with fitting a model from their empirical data because there isn't a reliable standard model to work off of (like intergenic distance between genes in an operon) that kind of challenge makes less engaging work worth the hassle. Maybe I'm odd because I've worked hard to have a good background in both statistics and biology, but I shouldn't be.

Although here is an observation that perhaps supports some of the intent of the article from my own experience. I was speaking with a biology graduate student and it came up that they had a biostatistics course in the department. Of course as a statistician my mind goes towards survival function, failure rate, life tables, censored data, bioassy, epidemiology, microarrays, clincal trials, topics along those lines. It turned out their course focused z tests, t tests, f tests, confidence intervals, point predictions, least squares regression, multiple regression, ANOVA, and things along these lines just with simulated problems in a lab setting. That is not necessarily a bad thing, but much of the core math was under played or missing like model assumptions and alternate formulations or things like dummy variables. The worst part was that even though they were doing well with the class they had no confidence in actually using the statistics and didn't understand how to interpret the meaning of something like a confidence interval, they knew how to calculate one, but it wasn't clear what it actually meant to them.

The corollary to the notion in the summary I'd rant and claim is that scientists overall have less than desirable skills in mathematics, statistics, and computation than those who studied those disciplines principally and that's hurting science. However many in those three disciplines really know little beyond basic results in any of the sciences which hurts the applicability of these mathematical fields to the sciences and likely hurt our ability to develop certain types of discipline specific results that can be generalized from work in application problems.

In either case whether you're a typical scientist or a typical math/stat/comp person in order to become proficient enough in the other areas it requires going an awfully long out of the way compared to any counterpart who simply does not care and goes straight through as many before have. While in some areas of research on either side it is no problem to do as has been done and not further knowledge into those other areas. Increasingly results that have the highest levels of impact are coming more and more from truly interdisciplinary research. In order to further encourage that for those who are interested in such fields (aside from making more clear what areas in any of the fields fringe to such interdisciplinary work) we need more incentive to study more than one field and/or better ways of enabling fruitful cooperation between the camps.

EMI and other Cope programs have MIDI output capability. He'd pipe the midi files to a program that allowed for printing as sheet music (I don't use apple's so I am not familiar with what program specifically he was sending the midi to for sheet music printing). The EMI described in Cope's books required manually keying in entries for the databases, but it would seem Zenph's only difference is that it is working with the audio capture directly. I'm pretty positive EMI or the other Cope programs could readily be adapted to do that. Also if EMI were only given a few pieces or a single piece in the analysis db and the parameters are set in a certain way EMI would do exactly what Zenph does.

I agree though that Zenph seems to want to head toward what EMI is already capable of. This gets compounded when you consider the style signatures or genetic analogies used by Cope or even a system like Pandora (which has been my impressions about what Recombinant might be all about) then it makes Zenph all the more trivial, but it points towards the increasing trend in this section of industry trying to do this sort of algorithmic composition, re-arrangement, remastering, etc.

I don't think that's a correct interpretation of copyright law.

"In the case of works made for hire, the employer and not the employee is considered to be the author." from LOC copyright circular

So if work for hire allows for corporations to create and author copyright materials then why wouldn't a corporation be able to author the copyright of the output of this sort of program?

David Cope's Experiments in Musical Intelligence and related works (SARA, other works, and his own company called Recombinant inc ) have been doing this for many years.

SIAM (Society for Industrial and Applied Mathematics) has several special interest groups related to computing/programming problems. The other major math and stat groups have excellent articles on computing problems from time to time as well like the AMS, MAA, or AMSTAT, but SIAM probably provides the most of these groups and a lot of coverage that compliments IEEE and ACM. Also depending on if you're working in a specific industry or if you're furthering your studies in a graduate program there may be other professional societies that deal with informatics or computational issues related to that focus.

As did ER "Heart of the Matter" (Season 6 Episode 273) http://en.wikipedia.org/wiki/List_of_ER_episodes

Which predates the others since its from 2006. The wikipedia article for broken heart syndrome also mentions it was referenced as a diagnosis in Grey's Anatomy, but I can't find that episode.

This was the diagnosis in episode 121 of Scrubs "My House"

Which was a parody of Dr. House by Dr. Cox in general but borrowed the following week's House diagnosis, episode 311 of the House MD "Words and Deeds"

back in good ole January 2007. WSJ is so behind the times. ;)

I know you can answer Jedi on a census in some countries apparently the UK is one of them, but I don't know if it is given all of the recognition of other religions. It could I suppose if the wikipedia numbers are accurate than that would count in many countries. Otherwise if it has such protections than this company has probably just ran afoul of the law and this young Jedi will be getting some cash out of it.

Charlie Eppes: Larry, something went wrong, and I don't know what, and now it's like I can't even think.

Larry Fleinhardt: Well, let me guess: you tried to solve a problem involving human behavior, and it blew up in your face.

Charlie Eppes: Yeah, pretty much.

Larry Fleinhardt: Okay, well, Charles, you are a mathematician, you're always looking for the elegant solution. Human behavior is rarely, if ever, elegant. The universe is full of these odd bumps and twists. You know, perhaps you need to make your equation less elegant, more complicated; less precise, more descriptive. It's not going to be as pretty, but it might work a little bit better. Charlie, when you're working on human problems, there's going to be pain and disappointment.You gotta ask yourself, is it worth it?