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montecarloop

Utility library for caching and analysis of estimates from Monte Carlo simulations.

Purpose

Monte Carlo simulations generate random samples based on some theoretical probabilistic model. A numerical fact of the theoretical model can be estimated from the random samples generated.

montecarloop provides features for two challenges with Monte Carlo estimation:

  1. long computation times
  2. accuracy of estimates

Mitigating long computation times

Monte Carlo simulations often make an extreme trade-off between estimation accuracy vs computation time. montecarloop can help make this trade-off less painful, especially when doing exploratory data analysis of slow Monte Carlo estimates within Python notebooks.

montecarloop can cache running statistics of Monte Carlo estimates into a file. A slow long running script can run Monte Carlo calculations independent of Python notebooks which can read and process preliminary estimates without being blocked.

Evaluation of estimator accuracy

After running a large nubmer of independent Monte carlo simulations, montecarloop will calculate statistics on the estimates, such as the standard deviation of the estimates. Furthermore, if montecarloop is provided a function which calculated the theoretical expected value of estimate, a p-value from a t-test will be performed. By running simulations for long periods of time, subtle statistical biases can potentially be found.

Quick Start

python3 -m pip install git+https://github.com/NovembreLab/montecarloop.git

Checkout the example notebook and script at github.com/NovembreLab/montecarloop/example/.

Columns of data table returned by Dealer.summary()

  • stat: the name of the statistic returned by monte carlo simulation estimator
  • num: number of monte carlo estimates calculated
  • mean: the average value of the num estimates calculated
  • std_dev: the standard deviation of the num estimates calculated
  • std_err: the estimated standard deviation of an average of num random estimates
  • @...: parameters passed to the monte carlo simulation estimator

if Dealer.summary(theoretical_calc) called with a parameter

  • null_hypo: the null hypothesis estimate returned by theoretical_calc
  • pvalue: the p-value from a two-tailed t-test