Experimenting with the transition state matrix approach to credit default modeling.
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Updated
Jun 9, 2017 - R
Experimenting with the transition state matrix approach to credit default modeling.
Share Market Prediction App using Markov Chains Model
Create sparse transition matrices given state-space vectors, mean, variance
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.
Continuous Time Markov Chain for daily panel data and annual transition probabilities
Continuous Time Markov Chain
Word suggestion based on the Markov Chain model
Library to find the Probability Estimation of Navigation Paths and their Pattern Prediction.
Simple and Modiifed implementation of PageRank in Python using Numpy .
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.
Application of Markov Chain in Finance
A Monte Carlo simulation representing the daily behaviour of customers inside a fictional supermarket. Featuring a colourful and clear visualisation interface.
Reinforcement Learning Using Q-learning, Double Q-learning, and Dyna-Q.
NPM package to easily create and use Markov chains
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.
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).
A Markov-chain based supermarket simulation.
Modeling and visualization of the movement of supermarket visitors based on real customer data.
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 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.
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