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# Doctoral Program - Courses

### Prerequisites

### Core Courses

### Literature/Work In Progress Course

### Advanced Courses

### Consulting Workshop

Ph.D. students register for 10 units in each of the autumn, winter and spring quarters. Most courses offered by the department for Ph.D. students are 3 units, including the core courses of the first year program. In addition to regular lecture courses on advanced topics, reading courses in the literature of probability and the literature of statistics are available each quarter. Students working on their dissertation may register for up to 10 units of directed research in each quarter. Students should also register for selected courses outside the statistics department in order to fulfill the breadth requirement.

Equivalents of Math 113, Math 115; Stats 116, Stats 200; CS 106A. (Descriptions of these courses may be viewed on Stanford's ExploreCourses course listings pages. Note that up to and including academic year 2015-16, the Stats305ABC sequence is called Stats305, Stats306A and Stats306B respectively).

Previous experience has shown that before starting the core courses students need to have mastered the material in the prerequisite courses (or their equivalents at other universities), as demonstrated by very strong and relatively recent grades. Where this background is missing or not recent, admission to the Ph.D. program will involve working with the Graduate Director to design an individual program to make up the necessary courses.

Statistics 300A, 300B and 300C systematically survey the ideas of estimation and of hypothesis testing for parametric and nonparametric models involving small and large samples.

Statistics 305A is concerned with linear regression and the analysis of variance.

Statistics 305B and 305C survey a large number of modeling techniques, related to but going significantly beyond the linear models of 305A. *(Note that up to and including 2015-16 the 305ABC series was numbered as 305, 306A and 306B)*.

Statistics 310A, 310B and 310C are measure-theoretic courses in probability theory, beginning with basic concepts of the law of large numbers, and martingale theory.

Although the content of the first year core courses is specified by the department, the order in which topics are studied and details of the presentation are left to the instructor and will vary from year to year. Unusually well prepared students may place out of Statistics 305. Students who do not have a sufficient mathematics background can, with approval from the Graduate Director, take the 310 series after the first year. All must be taken for a letter grade.

Stats 319 is a literature course in statistics and probability that is offered each quarter. The course is generally taken by students in the second and third years, and may be taken repeatedly. It serves two connected purposes:

- to expose students to a variety of topics of current research interest, for example, to help identify dissertation topics. Students are expected to read a number of articles and to write a short paper related to the reading that is presented to the class. The paper can be a synthesis of the reading material, or it may mark the beginning of research in the area. Reading assignments are made in consultation with any faculty member, especially the course instructor.
- to fulfill the Work in Progress requirement. Each post-quals and pre-orals student gives a 50 minute talk once a year. This requirement gives the student practice in giving and receiving feedback on talk technique, and keeps the department informed on the student's work. The talk can be on dissertation work in progress, on an ancillary project (consulting, RA work), or on a selection of papers that the student has recently read. The instructor of the literature course, along with the student's course peers, provide feedback on the talk, and can also provide guidance in topic choice where needed.

All students who have passed the qualifying exams but have not yet passed the Dissertation Proposal Meeting must take STATS 319 Literature of Statistics at least once per year.

The department offers advanced lecture courses on a number of topics including:

- Information Theory and Statistics (Stats 311)
- Advanced Statistical Methods (Stats 314A)
- Modern Applied Statistics: Learning (Stats 315A)
- Modern Applied Statistics: Data Mining (Stats 315B)
- Stochastic Processes (Stats 317)
- Modern Markov Chains (Stats 318)
- Function Estimation in White Noise (Stats 322)
- Multivariate Analysis (Stats 325)
- Topics in Probability Theory (Stats 350)
- Topics in Mathematical Physics (Stats 359)
- Monte Carlo (Stats 362)
- Statistical Models in Genetics (Stats 367)
- Baysian Statistics I (Stats 370)
- Convex Optimization I (EE 364A)
- Convex Optimization II (EE 364B)

In any given year only some of these courses will be offered. See also the Stanford Bulletin.

These courses are normally taken after the first year and may help students to find dissertation topics.

Students taking the consulting workshop, Stats 390, provide a free consulting service to the Stanford community. Researchers from all areas of the University drop in to discuss their problems. This course allows students to assimilate the material from their first year courses, especially Stats 305A/B/C.

The consulting is executed by teams of students, in which inexperienced students are matched with those more experienced. The course is offered each quarter and may be taken repeatedly. Students are encouraged to participate in the formulation of the consulting problems and in any data analysis which may be involved.