Pinned Repositories
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
rainbowfyqfly's Repositories
rainbowfyqfly/36-350
2018_CMU Statistical Computing
rainbowfyqfly/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.
rainbowfyqfly/AdjointMonteCarlo
rainbowfyqfly/AppliedPredicitveModeling_Exercises
Exercises for the book Applied Predictive Modeling by Kuhn and Johnson (2013)
rainbowfyqfly/AuroraPaper
Reproduction code for "Empirical Bayes mean estimation with nonparametric errors via order statistic regression"
rainbowfyqfly/CompFinance
Companion code for "Modern Computational Finance: AAD and Parallel Simulations" (Antoine Savine, Wiley, 2018)
rainbowfyqfly/DataScienceNotebooks
Tutorials about Machine Learning and Deep Learning
rainbowfyqfly/deepxde
Deep learning library for solving differential equations
rainbowfyqfly/Double-Machine-LearningonGitHub
rainbowfyqfly/FBSNNs
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
rainbowfyqfly/finmath-forward-initial-margin
rainbowfyqfly/Foundations
Foundations of Statistics and Machine Learning.
rainbowfyqfly/FraudDetection
Examples and Tutorials related to fraud detection with machine learning and deep learning
rainbowfyqfly/Gaussian-Process
GP Regression and Classification
rainbowfyqfly/GP-CVA
rainbowfyqfly/IPythonScripts
Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning
rainbowfyqfly/ISLR-Answers
Solutions to exercises from Introduction to Statistical Learning (ISLR 1st Edition)
rainbowfyqfly/MachineLearningStocks
Using python and scikit-learn to make stock predictions
rainbowfyqfly/ML_Finance_Codes
Machine Learning in Finance: From Theory to Practice Book
rainbowfyqfly/muRisQ-ir-models
muRisQ Advisory: Interest Rate Models for Derivatives.
rainbowfyqfly/RbyExample
R code and supplements to the book "R by Example" by Jim Albert and Maria Rizzo
rainbowfyqfly/SCR
Resources for the book "Statistical Computing with R"
rainbowfyqfly/StatComp
2013CMU 36-350 "Statistical Computing" Solutions to Labs and HWs
rainbowfyqfly/ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
rainbowfyqfly/Udacity-Machine-Learning-Nanodegree
All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.