Statistical analysis and visualization of state transition phenomena

Share Market Prediction App using Markov Chains Model

Application of Markov Chain in Finance

Continuous Time Markov Chain

A Markov-chain based supermarket simulation.

This application makes predictions by multiplying 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 a number of steps.

Create sparse transition matrices given state-space vectors, mean, variance

The Markov Chains - Simulation framework is a Markov Chain Generator that uses probability values from a transition matrix to generate strings. At each step the new string is analyzed and the letter frequencies are computed. These frequencies are displayed as signals on a graph at each step in order to capture the overall behavior of the MCG.

This application uses a transition matrix to make predictions by using a Markov chain. For exemplification, the values from the transition matrix represent the transition probabilities between two states found in a sequence of observations.

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.

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.

NPM package to easily create and use Markov chains

Simple and Modiifed implementation of PageRank in Python using Numpy .

Scripts supporting the Open Risk Academy course Analysis of Credit Migration using Python TransitionMatrix

Word suggestion based on the Markov Chain model