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Tutorial 2: Hidden Markov Model

Difficulty level

This tutorial covers how to simulate a Hidden Markov Model (HMM) and observe how changing the transition probability and observation noise impact what the samples look like. Then we'll look at how uncertainty increases as we make future predictions without evidence (from observations) and how to gain information from the observations.


Overview of this tutorial:

  • Build an HMM in Python and generate sample data
  • Calculate how predictive probabilities propagates in a Markov Chain with no evidence
  • Combine new evidence and prediction from past evidence to estimate latent states
Topics covered in this lesson
  • Introduction to the Hidden Markov Model (HMM) which can model dynamics between discrete states
  • Simulation of HMM with Gaussian observations
  • interference in a dynamic world
  • HMM for Poisson spiking neuronal network
  • Expectation-Maximization algorithm for HMM

Experience with Python Programming Language.

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