Introduction to stochastic processes and probabilistic modeling, with applications to biological systems, neural activity, and dynamic data-driven systems.
Focus: Probability · Markov Processes · Stochastic Simulation · Dynamic Systems
Methods: Markov Models · Stochastic Simulation · Time-Series Analysis · Probabilistic Modeling
Applications: Neuroscience · Systems Biology · AI · Sequential Data Modeling
Level: Undergraduate / Graduate
Core principles of probability theory for modeling uncertainty in complex systems.
Access materialsModeling discrete events and arrival processes in time.
Access materialsModeling state transitions in stochastic systems and population dynamics.
Access materialsStochastic modeling of growth, reproduction, and cascading processes.
Access materialsSimulation of stochastic chemical and biological systems at the molecular level.
Access materialsAnalysis of neural activity using stochastic modeling and time-series data.
Access materials Access dataAnalysis of cancer develpment using Moran process modeling.
Access materialsApplication of Markov models to biological sequence analysis.
Access materialsModeling hidden states in sequential data using probabilistic graphical models.
Access materialsHands-on implementation of stochastic simulations for dynamic biological systems.
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