Course notes and resources for Stanford University graduate lecture course PHYS366.

The course is intended to provide an introduction to modern statistical methodology and its applications to problems in astrophysics and cosmology. The course is aimed at graduate students intending to do research in astrophysics and cosmology, and we strongly encourage most first and second year students working in KIPAC to take the course. Our goal is to provide a background that will be directly relevant to the kind of problems that typical KIPAC students will encounter in their research.

Our goal is that students taking this course will:

• develop familiarity in working with various types of astronomical data.
• understand the role of modeling in data analysis.
• develop facility with various types of inference from data.
• be able to critically evaluate and apply commonly used statistical methodologies.
• be able to apply advanced statistical reasoning to problems they are likely to encounter in their research.

• Prerequisites
• Schedule
• Getting Started
• Textbooks and Course Materials
• Course Requirements
• Homework

• Exploring Data
• Understanding from Data
• Inference in Practice: PDF Characterization
• Inference in Practice: Sampling Techniques
• Inference in Practice: Coping with Complications
• Inference in Practice: Evaluating Models
• Applications in Astroparticle Physics
• Applications in Cosmology
• Machine Learning
• Project Presenations

You can help write a glossary of terms used in the lectures here.

• Phil Marshall
• Risa Wechsler

All materials Copyright 2015 Phil Marshall, Adam Mantz, Elisabeth Krause, and Risa Wechsler, and distributed for copying and extension under the GPLv2 License. If you have any feedback for us, please write us an issue. If you would like to help us improve this course, please do fork this repo and submit a pull request.