## Controlling Type I Experimental Error

Full disclosure: I’m currently taking a high-speed graduate class in Experimental Design from the Colorado State University statistics department. Right now, we’re working on methods for controlling Type I errors (“false alarms”) in cases where you might want to do multiple hypothesis tests.

For example, if you are testing sample means representing patient responses to three different disease treatments plus a fourth control treatment, you might want to test each individual disease treatment mean vs. the control mean to see whether patient responses under treatment are significantly different. This gives a total of at least 3 separate hypothesis tests. If you’ve designed each test so that your Type I error rate (the rate at which you erroneously decide that the treatments represent a genuine improvement when they really don’t) is capped at some value, usually 5%, then your overall Type I rate will be higher, maybe even as high as 15%. So some adjustment has to be made to your process to keep the overall Type I error rate controlled.

This writeup was done in an effort to collect and synthesize all the different methods for measuring and controlling Type I error into a digestible package. In short, I wrote this for my own understanding because, for some reason, I think much better about things when I write about them (because how can I know what I’m thinking until I see what I write?). I hope you find it useful.

## How I think about the neural network backpropagation algorithm

My husband Bernie says that every mathematician has a favorite mathematical object, and if that is so, then my favorite mathematical objects are matrices. I try to chunk all my mathematical understandings into matrix expressions, essentially translating everything into my native language. It makes it easier for me to understand things, and to remember what I’ve understood.

In this writeup, I derive the backpropagation algorithm, which is an implementation of the chain rule for certain kinds of composite functions, in terms of matrices and matrix products.

This writeup owes a lot to the chapter on backpropagation in Michael Nielsen’s online book, “Neural Networks and Deep Learning“. It is just a slightly different way to look at things that sticks in my head a little better.

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## Mathematical Derivation of the Extended Kalman Filter

This writeup is an extension of the writeup I posted last week, Mathematical Derivation of the Bayes and Kalman Filters. Both these writeups were written when I was studying Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard, and Dieter Fox; I think that book is awesome, and I really wanted to understand all the mathematical details. This writeup should be viewed as a supplement to Chapter 3.3 in that book.

The Extended Kalman Filter is an extension of the basic Kalman filter, which requires linear transition models and measurement models for each step, to the case where the transition and measurement models are nonlinear. EKFs aren’t as widely applicable as certain other popular Bayes Filter methods (cough particle filters), because they can’t represent as wide a range of types of belief distributions. Still, they work well for certain problems and apparently are widely used in practice.

Next week I’ll post something different, but first I needed to get all the Kalman Filter stuff out of my system. Do you have any requests for writeups on other applied math topics?

This is a “level 3” writeup: for grad students and hardcore practitioners.