A Monte Carlo simulation representing the daily behaviour of customers inside a fictional supermarket. Featuring a colourful and clear visualisation interface.
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Updated
Oct 14, 2021 - Jupyter Notebook
A Monte Carlo simulation representing the daily behaviour of customers inside a fictional supermarket. Featuring a colourful and clear visualisation interface.
Simulates the movement of players around the board for a game of US Standard 2008 Edition Monopoly, using a Markov process, in order to model the likelihood of landing on each tile.
The transition matrix of a Markov chain is a square matrix that describes the probability of transitioning from one state to another.
Scripts supporting the Open Risk Academy course Analysis of Credit Migration using Python TransitionMatrix
NPM package to easily create and use Markov chains
Simple and Modiifed implementation of PageRank in Python using Numpy .
Word suggestion based on the Markov Chain model
Markov Chain-Based Financial Prediction System
Modeling and visualization of the movement of supermarket visitors based on real customer data.
C++ header-only library for the full family of Xoshiro/Xoroshiro random number generators
The current JS application is a detector that uses observation sequences to construct the transition matrices for two models, which are merged into a single log-likelihood matrix (LLM). A scanner can use this LLM to search for regions of interest inside a longer sequence called z (the target).
Library to find the Probability Estimation of Navigation Paths and their Pattern Prediction.
WeatherChance is an open-source application that can predict whether the tomorrows weather of particular queried location/city will be good or bad. Good weather is essentially defined as sunny and less cloudly and bad weather is defined as rainy, snowy etc.
We have 4 different display advertising campaigns. We would like to evaluate how effective each advertising campaign is in generating sales
Continuous Time Markov Chain for daily panel data and annual transition probabilities
Computing and styling transition matrices with Python: a real-world application on Fortune Global 500
Reinforcement Learning Using Q-learning, Double Q-learning, and Dyna-Q.
Predictions with Markov Chains is a JS application that multiplies a probability vector with a transition matrix multiple times (n steps - user defined). On each step, the values from the resulting probability vectors are plotted on a chart. The resulting curves on the chart indicate the behavior of the system over n steps.
Analysis of robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Reweighting and T-revision. We demonstrate methods for estimating the transition matrix in order to obtain better classifier performance when working with noisy data.
Experimenting with the transition state matrix approach to credit default modeling.
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