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
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.
Computing and styling transition matrices with Python: a real-world application on Fortune Global 500
Scripts supporting the Open Risk Academy course Analysis of Credit Migration using Python TransitionMatrix
The transition matrix of a Markov chain is a square matrix that describes the probability of transitioning from one state to another.
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.
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.
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.
Modeling and visualization of the movement of supermarket visitors based on real customer data.
A Markov-chain based supermarket simulation.
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).
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.
NPM package to easily create and use Markov chains
Reinforcement Learning Using Q-learning, Double Q-learning, and Dyna-Q.
A Monte Carlo simulation representing the daily behaviour of customers inside a fictional supermarket. Featuring a colourful and clear visualisation interface.
Application of Markov Chain in Finance
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.
Simple and Modiifed implementation of PageRank in Python using Numpy .
Library to find the Probability Estimation of Navigation Paths and their Pattern Prediction.
Word suggestion based on the Markov Chain model
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