**Bayes Rule Refresher**

Here's another useful way to state the Bayes' rule for conditional probability (it just expands on what the OP wrote):
Note that here (1) is just the definition, (2) is a simple application of Bayes rule that we already know, and (3), (4) are various ways to rewrite (1) using factorization rules of the type P(ABC) = P(A|BC)P(B|C)P(C). Mentally, I find the following procedure useful:
- Pick the set of variables that I want to always fix as conditional (in Eq. (3) it's the event CD and in Eq. (4) it's D),
- Write the Bayes' rule
*as if these events didn't exist*(i.e. for Eq. (3) I would just run the Bayes rule for P(A|B)). - Rewrite the result, putting my "always conditioned-on" events behind the conditioning bar for every P(...) expression that I have.
This makes sense intuitively, if you think of conditioning as procedure of renormalizing the sample space in various ways. It's reasonable that you should be able to use Bayes' rule in the same way whether or not the probability space has been renormalized by conditioning.
answered
24 Oct '11, 15:35 |

**Choosing a statistical test**

http://imgur.com/Ctug4Dr

**Aikake Information Criterion**

*Maximum Likelihood*

$AIC = -2*logL(\theta|y) + 2k$

$k = $ total number of parameters

*Least Squares*

$AIC = n*log(\frac{RSS}{n}) + 2k$

$RSS = SSE = \sum(y - h(x))^2$

$ n = $ number of samples

http://www4.ncsu.edu/~shu3/Presentation/AIC.pdf

http://en.wikipedia.org/wiki/Residual_sum_of_squares

**Matrix Form Pointwise Distances**

$d_ij = ||x_i - y_j||^2 = ||x_i||^2 + ||y_j||^2 - 2<X_i,y_j>$

means that

$D = X + Y - 2X'Y$

Take the norm of X and Y i.e. X*X' or dot(X,X')

Normalize and calculate covariance

A * A.T (Hermitian!) / sqrt(diag(A.T * A) * diag(A.T * A).T)

http://statinfer.wordpress.com/2011/11/14/efficient-matlab-i-pairwise-distances/

**Rolling stats**

http://stackoverflow.com/questions/1058813/on-line-iterator-algorithms-for-estimating-statistical-median-mode-skewnes

**Current Links for Stats in Python**

http://r.789695.n4.nabble.com/Ornstein-Uhlenbeck-td2991060.html

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

http://blog.yhathq.com/posts/estimating-user-lifetimes-with-pymc.html

http://robjhyndman.com/hyndsight/crossvalidation/

https://www.leinenbock.com/tag/statsmodels/

http://nbviewer.ipython.org/urls/raw.github.com/carljv/Will_it_Python/master/MLFH/ch8/ch8.ipynb

http://nbviewer.ipython.org/urls/github.com/weecology/progbio/raw/master/ipynbs/statistics.ipynb

http://wiki.answers.com/Q/What_does_a_high_t_statistic_mean

http://stackoverflow.com/questions/13452353/multiple-correlation-in-python

http://stats.stackexchange.com/questions/4422/small-sample-linear-regression-where-to-start

http://nbviewer.ipython.org/urls/raw.github.com/carljv/Will_it_Python/master/MLFH/ch8/ch8.ipynb

http://nbviewer.ipython.org/urls/github.com/weecology/progbio/raw/master/ipynbs/statistics.ipynb

http://wiki.answers.com/Q/What_does_a_high_t_statistic_mean

http://stackoverflow.com/questions/13452353/multiple-correlation-in-python

http://stats.stackexchange.com/questions/4422/small-sample-linear-regression-where-to-start