Where are we and what should I be doing?
April 1:
In the midst of the hierarchical normal mean example of a high dimensional Gibbs sampler.
March 25:
About to do discrete theta in the Importance Sampling script.
Next, mu|sigma and sigma | mu in the bayes review, then Gibbs sampling.
March 18
Just started SIR (sampling, importance, resampling).
March 16
About to do conjuate prior for Bernoulli data.
After Bayes intro, we go back to Importance Sampling.
March 11
About to do intuition for rejection sampling.
No homework due at this time.
March 4
We are about to do 7. More on the EM, in the EM notes.
Let's just make homework 4 due March 11.
February 18:
finished svd.
start EM next time.
Hw 3 due Tuesday February 23.
February 25:
Just finished EM for univariate normal mixtures.
Homework 4 is on the webpage and due March 9th.
February 16:
We stopped at checking for normality in the Multivariate Normal, Eign, Cholesky notes.
February 9:
About to do the section on the Cholesky decomp details in the notes
on the multivariate normal and the cholesky/spectral decompositions.
Homework 2 due this Friday, February 12.
February 4:
Finished Quick review of Linear Algebra notes.
Homework 2 on webpage, due February 12.
February 2:
About to do the determinant in the Linear Algebra notes.
January, 28:
Ended at Cauchy Schwartz inequality.
January, 26:
finished at project y on x (single vectors).
January, 21:
Finished R version of simple script to compute logit likelihood.
Homework 1 is on the webpage and due February 2.
January, 19:
We just started looking a the R script to compute the simple logit likelihood.
January, 14:
We finished the Python hello world and looked at Rstudio and Rmarkdown.
Next we will go through the R Hello world.
Right now you need to be deciding what software you will use to take
the class. For example, we are looking at R and python.
Notable alternatives are Matlab and Julia, put I don't know if they support
all the tools we need.
If you have to learn R, have a look at the links on the webpage.
I think swirl is the easiest way to go.
I'm not sure what is the best way to learn python.
Some of the links I have on my python page look pretty good, for example the
A Whirlwind Tour of Python, by Jake VanderPlas
(A Whirlwind Tour of Python, I really like the book).
Again, the help on the Python/Numpy/Pandas/Scikit Learn/ pages is pretty impressive
and the help links in the Jupyter Notebook look great as well.
For my research I use a combination for R and C++.
I just been picking up Python "randomly".
Overall, I think R is easier, but if you have a programming background you might have a preference for Python.
Clearly, R has more statistics, but Python has scikitlearn and the neural net stuff seems to more a python thing.