Statistical analysis and visualization of state transition phenomena
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Updated
Apr 18, 2024 - Python
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.
Modeling and visualization of the movement of supermarket visitors based on real customer data.
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.
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.
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
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