Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

It is a jupyter notebook which examine the varience and bias parameters of maximum likelihood and maximum a posteriori approaches for biomedical imaging.