A discrete-time Python-based solver for the Stochastic On-Time Arrival routing problem
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Updated
Jan 1, 2022 - Python
A discrete-time Python-based solver for the Stochastic On-Time Arrival routing problem
Set of Jupyter (iPython) notebooks (and few pdf-presentations) about things that I am interested on, like Computer Science, Statistics and Machine-Learning, Artificial Intelligence (AI), Financial Engineering, Optimization, Stochastic Modelling, Time-Series forecasting, Science in general... and more.
Stochastic SIR models; adding age-structures and social contact data for the spread of covid-19. Lattice model for identifying and isolating hotspots. This has been further developed into a network(graph) of multiple clusters(lattices) and tracing the infection in such a population.
3rd Annual Undergraduate Quantitative Biology (UQ-bio) Summer School
Classical models implemented from a Markov operator's perspective
Weather Generators with Bayesian Networks
Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.
Code and data files necessary for reproducing cellular-automaton model of human spread across Sahul
C# pricer that allows users to price a wide range of financial products.
Adaptive Signal Processing (2020 Fall)
Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing
Bayesian inference of stochastic cellular processes with and without memory in Python.
🔢 Python module that calculates probabilities for a random walk in 1-dimensional discrete state space
Application of the ARIMA model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.
This script presents a simple stochastic description to model cell population distribution in the phases of the cell cycle
Application of the ETS model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahwalnagar District, Punjab, Pakistan.
Application of the ARIMA model to forecast PET patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahawalnagar District, Punjab, Pakistan.
Implementing Stochastic Models in Queuing Theory
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