Where are we and what should I be doing?
April 6:
We finished the notes on importance sampling (skipping adaptive rejection sampling).
We had started going through the example for prior sensitivity in the simple iid Bernoulli model.
April 4:
Just did quick look at Bayesian inference for the Bernoulli parameter.
Next time we do importance sampling.
March 28:
About to do the transformations section in the Monte Carlo notes.
March 16:
In the notes on mixture modeling.
Class ended at the results in the R package mclust for the simulated data.
February 28:
We stopped in the middle of section 8, Momentum, of the notes on optimization.
Homework 3 is on the webpage. It covers optimization.
I will put recorded lectures for sections 8 and 9 of the optimization notes on the webpage
so you can work on homework 3 now if you want.
No class on March 2 (no zoom or in-person).
February 27:
Homework 3 is on the webpage.
The homework asks to to apply the optimization ideas to the logit model.
February 16:
We are about to do matrix approximation in the notes on SVD.
February 9:
We just finished the fundamental properties of the multivariate normal using the Choleski decomposition.
Howework 2 is on the webpage.
February 7:
Just did univariate change of variables in the Choleski and Eigen notes.
February 2:
We are about to do the section on determinants.
January, 31:
We finished section 4, Matrics of the Linear Algebra notes.
January, 26:
We are working through the R script for computing and plotting the log likelihood for
simple logistic regression.
Homework 1 is on the webpage and due February 6.
January, 24:
We are working through the python script to compute and plot the logit log-likelihood.
January, 19:
We are just about to the the multiple regresson of y=price on x=(mileage,year) in the
Hello world python script.
You should be playing around in R and/or python and look at the various books and websites
to find python/R resources that work for you.
Everyone (whether you are using R or python) should have a look at
https://scikit-learn.org/stable/.
In particular, have a look at:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.
Note the statement:
Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization.
What are the problems with OLS????!!!!
It is also useful to look at the official Machine Learning in R webpage:
https://cran.r-project.org/web/views/MachineLearning.html.
January, 17:
We finished going through the hello world in R script, discussed python software, and started the hello world python script.
Next time (1-19-23) we will continue with the python script.
The R code I was look at is do_1-17-23.R
.
The python code I was looking at is do_1-17-23.py
.
January, 13:
We are working throught the "Hello World in R" document at
https://www.rob-mcculloch.org/R/R_Hello-World_Regression.html.
Let's pick it up next time at the section titles
Run the Regression of y=price on X=(mileage,year).
If you are planning to use R, make sure you have R and Rstudio installed and
start playing with R.
I you are planning to use python have a look at my python information page
https://www.rob-mcculloch.org/python/index.html
and get python installed.
You have to decide if you are doing anaconda, miniconda, or a system install (e.g. with pip3).
Hopefully next class we will wrap up our look at R and move on the python by going through
https://www.rob-mcculloch.org/python/Py_Hello-Word_Regression.html.
Raj (student) notes that on the mac home brew is a great package manager
Hey Prof,
Homebrew (Brew) is the go-to package manager for macOS. It can be used to install software libraries (e.g. Python or R), applications (e.g. RStudio), and managed services (e.g. postgres).
Command to install brew: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
There seems to be different flavors/variants of conda (Anaconda, Miniconda, etc.). I'm currently using Miniforge, which can be installed via brew install miniforge
- RD
January, 10:
First class.
Note that while I will be lecturing in class, the lecture will also be available on zoom and recorded.
zoom info:
Hi there,
Robert McCulloch is inviting you to a scheduled Zoom meeting.
Topic: Computational Statistics, Spring 2023
Time: Jan 10, 2023 12:00 PM Arizona
Every week on Tue, Thu, until Apr 27, 2023, 32 occurrence(s)
Jan 10, 2023 12:00 PM
Jan 12, 2023 12:00 PM
Jan 17, 2023 12:00 PM
....
Apr 20, 2023 12:00 PM
Apr 25, 2023 12:00 PM
Apr 27, 2023 12:00 PM
Join from PC, Mac, Linux, iOS or Android: https://asu.zoom.us/j/85321075213?pwd=QU80cjBqdTZSeDdUL2JZN2YyYlRQUT09
Password: 014016