Probabilistic Graphical Models

Course code: PROBMACLNR

This course is intended for people interested in Bayesian networks and probabilistic programming. The theoretical part at the beginning of the course will lead to a practical example of topic modeling using Latent Dirichlet Allocation and its non-parametric extension including hyperparameter estimation. By completing this course, the participants should be able to design and implement their own simple Bayesian networks for various problems.

210 EUR

254 EUR including VAT

The earliest date from 20.02.2024

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Course dates

Starting date: 20.02.2024

Type: Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 210 EUR


Starting date: Upon request

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 210 EUR


Type Course
Language Price without VAT
20.02.2024 Virtual 1 day en/cz 210 EUR Register
Upon request In-person/Virtual 1 day en/cz 210 EUR Register
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Course structure

  • Bayesian networks
  • Model representation
  • Generative vs. discriminative models
  • Statistical inference in Bayesian networks
    • Variational inference
    • Sampling
      • Rejection sampling
      • Markov Chain Monte Carlo
      • Metropolis-Hastings sampling
      • Gibbs sampling
  • Probability distributions
    • Binomial and multinomial distributions
    • Beta and Dirichlet distributions
    • Gamma distribution
  • Probabilistic programming languages
  • Practical example with topic modeling
    • Latent Semantic Analysis
    • Probabilistic Latent Semantic Analysis
    • Latent Dirichlet Allocation
  • Non-Parametric topic modelling
    • Dirichlet process
    • Chinese restaurant process and Stick breaking process
    • Non-parametric LDA
  • Hyperparameter estimation


  • Basic knowledge of programing in Python
  • High school level of mathematics

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