Lecture-notes

This repository contains lecture notes for the course:

Computational Methods for Physicists and Engineers PHYS 2030 Winter 2024 York University

Course instructor: Sean Tulin

Overview: The course is divided into three parts:

Table of contents (Lessons):

Name		Name	Last commit message	Last commit date
Latest commit History 103 Commits
data		data
figures		figures
.gitignore		.gitignore
ABC_decay.png		ABC_decay.png
DFT_fig.pdf		DFT_fig.pdf
LICENSE		LICENSE
Lesson 1 - Introduction to sampling.ipynb		Lesson 1 - Introduction to sampling.ipynb
Lesson 10 - Fourier series.ipynb		Lesson 10 - Fourier series.ipynb
Lesson 11 - Discrete Fourier Transform.ipynb		Lesson 11 - Discrete Fourier Transform.ipynb
Lesson 2 - Normal distributions.ipynb		Lesson 2 - Normal distributions.ipynb
Lesson 3 - Arbitrary sampling.ipynb		Lesson 3 - Arbitrary sampling.ipynb
Lesson 4 - Importance sampling.ipynb		Lesson 4 - Importance sampling.ipynb
Lesson 5 - Multivariate sampling.ipynb		Lesson 5 - Multivariate sampling.ipynb
Lesson 6 - Markov Chain Monte Carlo.ipynb		Lesson 6 - Markov Chain Monte Carlo.ipynb
Lesson 7 - Euler method.ipynb		Lesson 7 - Euler method.ipynb
Lesson 8 - Second-order methods.ipynb		Lesson 8 - Second-order methods.ipynb
Lesson 9 - Leapfrog method.ipynb		Lesson 9 - Leapfrog method.ipynb
Old Lesson 7 - MCMC for continuous distributions.ipynb		Old Lesson 7 - MCMC for continuous distributions.ipynb
Old Lesson 8 - Bayes theorem.ipynb		Old Lesson 8 - Bayes theorem.ipynb
README.md		README.md
Sampling_figure.png		Sampling_figure.png
Tutorial 1.ipynb		Tutorial 1.ipynb
Tutorial 2.ipynb		Tutorial 2.ipynb

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