Homework 2. Stat 202a. Due Tue Oct 28, 10:30am.
You must work on the homework INDEPENDENTLY! Collaborating with other students
on this homework will be considered cheating. Submit your homework by ema
/*
#
# Stat 202A - Homework 6
# Author: Anshita Mehrotra
# Date : 11/10/2016
# Description: This script implements sweep and QR
# operations in Rcpp
#
#
# INSTRUCTIONS: Please fill in the missing line
0. Bonev's info.1. Projects.2. Bias in sample variance?3. Making R packages.4. Calling R from C.0. Bonev's Python and object oriented programming info.Boyan Bonev's email is [email protected] slides
0. Announcements.1. Matrices in C.2. Python and MySQL references.3. Generalized additive models.4. Bias in the sample SD.0. Announcements.Reminder, HW3 is due this Thursday, 11/14 by email.Thu 11/14 w
0. Misc.1. Sum of squared differences from observations for each gridpoint.2. Kernel regression.3. Teetor ch. 11.4. Projects.0. Misc.Reminder, no class or OH Tue Nov 5.After today, you will have seen
0. Final projects and other miscellaneous notes.1. Defining vectors and matrices within C.2. C functions communicating.3. Running C from terminal.4. Reading in from a file.0. Final projects and other
1. Final projects.
2. R Cookbook ch. 11.
3. XCode.
4. Compiling C and calling C from R.
5. Hello world.
6. pi.
7. dnorm.
8. Sum of squared differences between observations.
1. Final projects.
I made
0. Nov 5, HW3, and final projects.1. pi.2. dnorm.3. Sum of squared differences between observations.4. Teetor ch. 11.5. Teetor ch. 13.1 and 13.2.0. Nov 5, HW3 and final projects.NO CLASS TUE NOV 5 due
1. Admin stuff.
2. Rejection sampling again.
3. R Cookbook ch. 8-9.
4. R Cookbook ch 10.
5. R Cookbook ch 11.
1. Admin stuff.
Reminder, no class or office hour Tue Oct 22.
We will switch to C next lec
1. Final projects and group assignment.
2. Kernel density estimation in R, continued.
3. 2-d kernel smoothing.
4. Simulating from a density by rejection sampling.
5. Maps.
6. R Cookbook ch. 8.
Reminde
1. Hw etc.
2. R Cookbook.
1. HW etc.
No lecture Tue Oct 22.
Hw2 is on the course website http:/www.stat.ucla.edu/~frederic/202a/F13
in word and pdf, and is be due Thu, Oct 31, 1030am.
Late homework
1. Administrative stuff.
Adding, prerequisites, etc.
The syllabus.
The good and bad things about this course, the book, and my teaching.
Grammar vs. vocabulary.
Read ch. 1-4.
2. R Cookbook.
Downloadin
1. Enrollment.
2. Plotting the sample mean.
3. R Cookbook.
1. Enrollment.
I'm not giving out any more PTE numbers.
If you are an unenrolled Statistics MS or PhD student, then please see me.
Note smal
1. Hw1.
2. pi.r.
3. R cookbook ch 1-2.
1. Hw1 is due Tue Oct 8 1030am by email.
See the course website, http:/www.stat.ucla.edu/~frederic/202a/F13 .
Read through ch4 for next class.
Read through ch.
0. HW4.1. MLE.2. MLE for Hawkes point processes.3. MLE using optim().0. HW4 is now due Wed Nov 27 1030am instead of Tue Nov 26.1. MLE. Instead of minimizing the sum of squared residuals as in regressi
Undergrad probability course
(not a poker strategy guide nor
an endorsement of gambling).
Standard undergrad topics +
random walks, arcsine laws, and
a few other specia
Overview
Before building
S3 classes
Building R Packages
An Introduction
David Diez
Biostatistics Dept
Harvard SPH
Packaging
Wrap-up
Overview
Before building
S3 classes
Packaging
Wrap-up
Original versi
#
# Stat 202A - Homework 5
# Author: Anshita Mehrotra
# Date : 11/03/2016
# Description: This script implements factor analysis and
# matrix completion
#
#
# INSTRUCTIONS: Please fill in the missing l
#
# Stat 202A - Homework 7
# Author: Anshita Mehrotra
# Date : 11/17/2016
# Description: This script implements PCA and logistic
# regression.
#
#
# INSTRUCTIONS: Please fill in the missing lines of c
"
Stat 202A - Homework 1
Author: Anshita Mehrotra
Date : 10/04/2016
Description: This script implements the sweep operator as
well as Gauss-Jordan elimination in both plain and
vectorized form
INSTRUC
#
# Stat 202A - Homework 3
# Author: Anshita Mehrotra
# Date : 10-20-2016
# Description: This script implements the lasso
#
#
# INSTRUCTIONS: Please fill in the missing lines of code
# only where spec
Stat 202a, Sta(s(cal Compu(ng. Prof. Rick Paik Schoenberg.
1. Logical operators in R.
2. Element selec(on and redeni(on in R.
3. Vector arithme(c and order of opera(ons i
Stat 202a, Sta(s(cal Compu(ng. Prof. Rick Paik Schoenberg.
1. Administra(ve things.
2. R very basics.
3. Output in R.
4. Func(ons in R.
5. Logical operators in R.
6.
Stat 202a, Sta(s(cal Compu(ng. Prof. Rick Paik Schoenberg.
1. Modes and lists.
2. Finding the sta(s(cal mode in R.
3. Working directories and libraries.
4. Input and out
# Find sqrt(sum of 1/k^2.1), for k = 1, 2, 3, .n = 100000x = rep(1,n)for (k in 2:n)cfw_ x[k] = x[k-1] + 1/k^2.1y = sqrt(x)par(mfrow=c(1,2) # do this if you want two plots per page.plot(c(0,n),c(min(y)
1. Hw etc.2. R Cookbook ch1-4, continued.3. R Cookbook ch5.4. R Cookbook ch6.1. HW2, etc.We will talk about kernel smoothing next week.Read up through ch7 in R Cookbook.Loading in the housing data.I t
Homework 1. Stat 202a. Due Thu, Oct 16, 10:30am. Late hws will not be accepted!
You must work on the homework INDEPENDENTLY! Collaborating on this homework
will be considered cheating. Submit your hom
1. Hw etc.
2. mean and apply.
3. R Cookbook ch. 5.
4. R Cookbook ch. 6.
5. R Cookbook ch. 7.
1. Hw etc.
Reminder, no class Tue Oct 22.
Like Hw1, hw2 involves doing some loops in R.
Loops are slow in R
PARALLEL COMPUTING
Using multiple computer resources to solve a
computational problem.
These notes were provided by Jacalyn Huband from
the University of Virginia
Why parallel computing?
Parallel Prog
Simplest Regression
Simplest regression Suppose we observe (xi , yi ), i = 1, ., n. The simplest linear regression model
is yi = xi + i .
Least squares principle The least squares estimator is defined