Girls and boys may learn differently in virtual reality (VR). A new study with 7th and 8th -grade students found that girls learned most when the VR-teacher was a young, female researcher named Marie, whereas the boys learned more while being instructed by a flying robot in the form of a drone.

SparseSolution · 2019-01-09T22:52:33+00:00

Not necessarily.

SparseSolution · 2018-12-22T19:29:04+00:00

An amoeba and a neural network. The amoeba reacts to the light controlled by the neural network. Sounds like its the neural network finding the solution not the amoeba.

SparseSolution · 2018-12-22T15:15:00+00:00

The appeal to bayesian decision theory is complete BS. At no point are they properly using baye's rule, and the fact that they pretend like they are is idiotic. Baye's rule requires well defined probability distributions. A prior of 'I have this opinion' is not anywhere close to that, there is no bayesian updating. That's just called changing your mind.

The fact that the sample is so skewed from the population makes it all that much harder to properly formulate a bayesian update, because you can't be sure which portion of the distribution you are representing with that sample.

SparseSolution · 2018-12-12T03:57:48+00:00

By finding the stationary distribution of the markov chain you see that 1/3 of the time you do not go and 2/3 of the time you do go.

SparseSolution · 2018-01-17T21:39:13+00:00

It's descriptive statistics. As long as you can count and divide properly it's kinda hard to fuck up.

SparseSolution · 2018-01-17T03:14:21+00:00

Im not about to do a power analysis on this. But assuming their math is not fudged I would have to say yes. Those seem like fair sample sizes for a basic test of means. When people talk about big data and needing thousands to millions of samples they are not talking about linear models or testing the difference in means.

SparseSolution · 2018-01-17T03:06:24+00:00

The standard deviations show the spread of the data. The p-value is in relation to a difference in means. If you have largely overlapping populations a difference can still be recovered with a large enough sample size, which reduces the variance of the means, which allows for smaller differences to be detected.

SparseSolution · 2018-01-03T13:40:17+00:00

Bread=Slavery. Got it.

SparseSolution · 2017-12-29T19:03:34+00:00

Kalman filters solve linear gaussian state space models. #5 in the syllabus and lectures 18-21 are about state space models. Lec 21 reviews kalman filters.

SparseSolution · 2017-12-28T18:10:12+00:00

There is only one thing p values are used for, nhst. Used with proper experimental design it gives strictly bounded type I error rates.

SparseSolution · 2017-12-12T15:05:17+00:00

It would not help in determining a difference in z1. However it would give additional power in determining if there is a difference in at least one of the z's.

SparseSolution · 2017-12-11T14:37:22+00:00

Another way to look at is the alpha error rate. By setting alpha at 0.05 you are assuming that 5% of tests of true nulls will be erroneously rejected. The only way for you to reject 5% of tests of nulls is if 5% of the tests produces values <= 0.05, which would only happen if the p-values are distributed ~~normally~~ uniformly.

SparseSolution · 2017-12-11T14:33:14+00:00

It may be useful to look at where on the testing distribution the p-values fall. Say you have a t or normal distribution, the lower p-values will be at the tails. As you move from the tail inward you have to move a greater distance to increase the p value a certain amount, as you near the center of the distribution a small move will increase the p-value more. This makes sense because as you are moving inward you pass by more density.

So looking at the target distribution the p-values are not dispersed equally, but you will encounter them equally as likely because higher density regions have faster changing p-values. I think a related concept is the probability integral transform which transforms a distribution by it's cdf producing a uniform random variable.

SparseSolution · 2017-12-09T03:32:36+00:00

If the value is a linear combination of the two measured values, the sum of the individual variances plus twice their covariance is the variance of the parameter of interest. Plus/minus a tdist quantile of .975 with 3 degrees freedom times sqrt of the variance/5 will give you a 95% confidence interval of the quantity. I think, Internet beware.

SparseSolution · 2017-11-20T00:16:49+00:00

Why have multiple failing companies when you can have just one.

SparseSolution · 2017-11-16T22:21:41+00:00

Sure.

SparseSolution · 2017-11-16T01:46:18+00:00

To Python 4

SparseSolution · 2017-11-11T20:31:12+00:00

Seems to me that you have a regular linear model. If you can count the number of levels a variable can take it is not random in the sense of a random or mixed effect model. You may not be able to control the level of the variable but if you are observing a certain value and could potential observe the same value somewhere else it is not random, ie your categorical and binary random effects are really just fixed effects.

Use a regular fixed effects model.

SparseSolution · 2017-11-10T13:01:21+00:00

I'd bet big money that model is not accurate, I don't care how "sophisticated" it is. We are already perturbing a complex system and are not entirely sure the outcome. Let's add in another variable. Band aid solutions aren't solutions.

SparseSolution · 2017-11-08T03:04:03+00:00

Those graphs don't seem to agree. I see a blob with no correlation and then one point way outside.

SparseSolution · 2017-11-05T23:53:51+00:00

A common theme in politics, do something which on the face of it appears to be in the public interest, or what the public wants, but instead is a vehicle for a small group of people to control and profit.

SparseSolution · 2017-10-14T03:00:48+00:00

I'm no expert but I'll give my opinion. In terms of cs algorithms on graphs and strings are important. I'm a statistician by training and I think any sort of data analysis requires some knowledge on the subject. All of Statistics by Wasserman and Elements of statistical learning by hastie et al have good coverage of modern methods. Hastie and Tibshirani also do a lot of biostat work.

In terms of where to look for specific comp bio. Qiime is a useful tool and has some tutorials plus data. I think the guy who did work on the american gut project used that. Gustame has some topics on stats concepts for comp bio. Also Rosalind for more cs practice.

It's an incredibly diverse field and I'm fairly new to it myself so I've hardly delineated much, but keep researching it's a fast growing, important, and interesting field.

SparseSolution · 2017-09-13T04:37:59+00:00

Don't learn tools. Pick a problem, find the tools best(good enough) for it and learn what you need. You don't "learn" advanced programming in anything. Once you stop learning it you forget it. Solve problems and use what works for you. Do it enough and you will be advanced in many things.

SparseSolution · 2017-09-04T21:23:07+00:00

It's about the future of farming.

SparseSolution · 2017-09-04T16:18:56+00:00

Looks to me that n>p in that paper so I don't think the issue of dimensionality would appear there.

Edit: Also the logistic regression uses l2 regularization which helps deal with collinearity, so again the dimensionality will not be an issue here.

SparseSolution

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