rainbowfyqfly Goto Github PK

36-350

2018_CMU Statistical Computing

a-beginner-guide-to-carry-out-extreme-value-analysis-with-codes-in-python

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

adjointmontecarlo

appliedpredicitvemodeling_exercises

Exercises for the book Applied Predictive Modeling by Kuhn and Johnson (2013)

aurorapaper

Reproduction code for "Empirical Bayes mean estimation with nonparametric errors via order statistic regression"

compfinance

Companion code for "Modern Computational Finance: AAD and Parallel Simulations" (Antoine Savine, Wiley, 2018)

datasciencenotebooks

Tutorials about Machine Learning and Deep Learning

deepxde

Deep learning library for solving differential equations

double-machine-learningongithub

fbsnns

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

finmath-forward-initial-margin

foundations

Foundations of Statistics and Machine Learning.

frauddetection

Examples and Tutorials related to fraud detection with machine learning and deep learning

gaussian-process

GP Regression and Classification

gp-cva

ipythonscripts

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

islr-answers

Solutions to exercises from Introduction to Statistical Learning (ISLR 1st Edition)

machinelearningstocks

Using python and scikit-learn to make stock predictions

ml_finance_codes

Machine Learning in Finance: From Theory to Practice Book

murisq-ir-models

muRisQ Advisory: Interest Rate Models for Derivatives.

rbyexample

R code and supplements to the book "R by Example" by Jim Albert and Maria Rizzo

scr

Resources for the book "Statistical Computing with R"

statcomp

2013CMU 36-350 "Statistical Computing" Solutions to Labs and HWs

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

udacity-machine-learning-nanodegree

All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.