monaco
elsim
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monaco
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ACX Grants ++: The First Half
At the heart of all serious forecasting is a statistical tool known as Monte Carlo analysis. It allows you to quantify uncertainty by introducing randomness to the inputs of computational models and looking at the range of results. If you want a good example, you might recognize Monte Carlo techniques from Nate Silver’s election forecasts at 538. It's been a gold-standard throughout my career in the space industry, and I can attest to how powerful it is - I've used it to successfully send a rocket to Mars. However, there aren't any tools out there that make it easy for researchers to take their existing models and wrap a Monte Carlo around it. So, I wrote one. It's an open-source python library which I'm calling "monaco". I'm at a point in development where the basic feature set is complete and working well, and I'm looking to finish up the extended roadmap in the next few months. See the project github page for the code, examples, and a lot more info: https://github.com/scottshambaugh/monaco. I’m looking for $1000 to help me present version 1.0 of this tool to the scientific community at the 2022 SciPy Conference in Austin, TX this summer. That amount should cover conference fees, hotel, and airfare, and if you're feeling generous I could use additional funds for some external monitors and cloud compute time. My name is Scott Shambaugh, and if you’re interested in helping fund this please email me at wsshambaugh AT gmail.com. Thank you!
elsim
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[Q] Do I correctly understand how to put error bars on a mean of true/false trials? It's so confusing.
No, this is a whole simulation with a true or false output for each Bernoulli experiment. Generate hundreds or thousands of points in N-dimensional space, calculate Euclidean distance between them, etc. This for example.
What are some alternatives?
rebop - Fast stochastic simulator for chemical reaction networks
Condorcet - Command line application and PHP library, providing an election engine with a high-level interface. Native support 20+ voting methods, easy to extend. Support simple elections with ease or billions of votes in low resource environment. Intensively tested and highly polyvalent.
emukit - A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Redistricting - QGIS Plugin containing tools for electoral delimitation
ebisu - Public-domain Python library for flashcard quiz scheduling using Bayesian statistics. (JavaScript, Java, Dart, and other ports available!)
quadelect - Voting method exploration/simulation tool
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
abcvoting - Python implementations of approval-based committee (multi-winner) voting rules
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
vse-sim - Methods for running simulations to calculate Voter Satisfaction Efficiency (VSE) of various voting systems in various conditions.
scikit-learn - scikit-learn: machine learning in Python
Condorcet-Voting-Open-Source-Ecosystem-Map - Collaborative index of preferential voting open-source projects. Related to Condorcet vote method an others.