Introduction to the fundamental ideas and techniques of probability theory and statistical inference.
Matlab
,
Mathematica
, R
, NumPy
) is
assumed.Probability will be covered in the first half of the term (using Pitman) and statistics (using Larsen and Marx) in the second half (see below for information regarding textbooks). Main topics covered are:
Section | Recitation | TA | Office Hours |
---|---|---|---|
1 | 9:00 am Thursday 159 Sloan |
Jake Marcinek jmarcine@caltech.edu |
153 Sloan 3 PM, Saturday |
2 | 9:00 am Thursday 153 Sloan |
Dmtri Gekthman dgekhtma@caltech.edu |
155 Sloan 2:30 PM, Sunday |
3 | 10:00 am Thursday 115 BCK |
Andrei Frimu afrimu@caltech.edu |
155 Sloan 3:30, PM Sunday |
4 | 10:00 am Thursday 101 Kerckhoff |
Nathan Lawless nlawless@caltech.edu |
155 Sloan 4:30 PM, Sunday |
5 | 10:00 am Thursday 19 Baxter |
Seunghee Ye syye@caltech.edu |
160 Sloan x4324 8 PM, Sunday |
6 | 11:00 am Thursday 151 Sloan |
Karlming Chen kchen7@caltech.edu |
155 Sloan Noon, Sunday |
7 | 1:00 pm Thursday 27 Gates |
Jim Tao jtao@caltech.edu |
155 Sloan 11 AM, Saturday |
8 | 1:00 pm Thursday 107 Downs |
Marius Lemm* mlemm@caltech.edu |
156 Sloan 5:30 PM, Sunday |
9 | 2:00 pm Thursday 27 Gates |
Pooya Vahidi pvahidif@caltech.edu |
155 Sloan 6 PM, Friday |
*Head TA
*Information is subject to change*
The required
textbooks for the course are:
There will be additional readings from time to time, either as handouts or articles available on line.
There are other books that you may find useful for this course or perhaps later in life. Here are some of my recommendations.
cookbookand reference geared toward biologists, but is a useful reference for almost everyone.
Modern statistical practice is computationally intensive, but
this course is not especially so. But you will have to use
computers to do some of the assignments. Many of
the people on campus that I have talked to recommend the statistical
programming language R
(the open source alternative to
AT&T's S
). The new Mathematica 9 and 10
claim to be highly integrated with R
, but I haven't
tried it yet. Others I have talked to rave about
NumPy
, an extension of Python
that
provides much of the functionality of Matlab
. Still
others continue to use other packages because they have invested a
lot of effort in learning to use them. (I myself often use
Mathematica
because I started using it in 1992, so my
recommendation of R
falls into the category of do as I
say, not as I do.)
My son recommends R
and this video as an
endorsement.
Here are a couple of highly recommended books on R
that I mostly have not read. But I find the first two to be useful.
Please report any errors or suspected errors or confusing bits to the professor. You can use the anonymous feedback form if you wish.