Pinned Repositories
AlphaTrading
An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
amusement-park-queuing-optimization
The main problem facing both amusement park customers and owners such as Disney World is customer satisfaction and efficiency, which are both negatively effected by high wait times. As a result, these parks have spent significant time and money to implement methods which reduce wait times to both increase customer satisfaction and efficiency. We explored the implementation of an reservation-dependent priority queuing system to devise how to best reduce average customer wait times for Expedition Everest, a popular ride at Disney World. Using third-party data, we first built constant-rate and time-dependent-rate queuing systems to model current behavior, followed by implementing an Express Queue into the system. We found a decrease in average wait time of 18.31% through simulating 30 days of typical customer behavior with the improvement strategy. Finally, we performed sensitivity analysis to optimize the parameters of our improvements, finding an ultimate optimal decrease in wait times of 44.42%.
angular-phonecat
Tutorial on building an angular application.
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
awesome-rl
Reinforcement learning resources curated
Barra-Model
An internship project: Implement Barra model to take risk or style factor attribution based on multi-factor model.
Barra-Multiple-factor-risk-model
Barra-Multiple-factor-risk-model
bayes-workflow-book
sources for book *Bayesian Workflow Using Stan", (working title)
bayes_days
R & Stan code associated with my "Bayes Days" workshop
BayesHMM
Full Bayesian Inference for Hidden Markov Models
zhaosongyi's Repositories
zhaosongyi/amusement-park-queuing-optimization
The main problem facing both amusement park customers and owners such as Disney World is customer satisfaction and efficiency, which are both negatively effected by high wait times. As a result, these parks have spent significant time and money to implement methods which reduce wait times to both increase customer satisfaction and efficiency. We explored the implementation of an reservation-dependent priority queuing system to devise how to best reduce average customer wait times for Expedition Everest, a popular ride at Disney World. Using third-party data, we first built constant-rate and time-dependent-rate queuing systems to model current behavior, followed by implementing an Express Queue into the system. We found a decrease in average wait time of 18.31% through simulating 30 days of typical customer behavior with the improvement strategy. Finally, we performed sensitivity analysis to optimize the parameters of our improvements, finding an ultimate optimal decrease in wait times of 44.42%.
zhaosongyi/bayes-workflow-book
sources for book *Bayesian Workflow Using Stan", (working title)
zhaosongyi/BayesHMM
Full Bayesian Inference for Hidden Markov Models
zhaosongyi/Bayesian-Analysis-with-Python-Second-Edition
Bayesian Analysis with Python - Second Edition, published by Packt
zhaosongyi/examples-1
Example code and files from "Prometheus: Up and Running"
zhaosongyi/geatpy
A high-performance GEA framework for Python. Welcome to star and fork.
zhaosongyi/gsoc17-hhmm
Bayesian Hierarchical Hidden Markov Models applied to financial time series, a research replication project for Google Summer of Code 2017.
zhaosongyi/Hands-On-Markov-Models-with-Python
Hands on Markov Models with Python, published by Packt
zhaosongyi/handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
zhaosongyi/handson-unsupervised-learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
zhaosongyi/Keras-GAN
Keras implementations of Generative Adversarial Networks.
zhaosongyi/Learning-Concurrency-in-Python
Learning Concurrency in Python by Packt
zhaosongyi/machine_learning_docker
zhaosongyi/ner_with_dependency
zhaosongyi/nlpia
Examples and libraries for "Natural Language Processing in Action" book
zhaosongyi/normalizing_flows
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
zhaosongyi/notes
Course notes
zhaosongyi/pmtk3
Probabilistic Modeling Toolkit for Matlab/Octave.
zhaosongyi/probabilisticprogrammingprimer
zhaosongyi/prometheus-book
Prometheus操作指南
zhaosongyi/prometheus-data-science
A collection of analysis, analytics, and machine learning techniques for time series forecasting w/ Prometheus metrics
zhaosongyi/prometheus-flatliner
zhaosongyi/proxy_pool
Python ProxyPool for web spider
zhaosongyi/pwc
Papers with code. Sorted by stars. Updated weekly.
zhaosongyi/pyhsmm
zhaosongyi/pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
zhaosongyi/qrm
qrm
zhaosongyi/rethinking
Statistical Rethinking course and book package
zhaosongyi/tutorials
zhaosongyi/uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2021