Probabilistic Graphical Models

MLC_PGM
Duration 1 day
0 ITK points
0 terms
Praha (4 990 Kč)

Brno (on request)

Bratislava (200 €)

This course is intended for people interested in Bayesian networks and probabilistic programming. At the beginning of the course, the theoretical part 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.

This course is intended for people interested in Bayesian networks and probabilistic programming.

basic knowledge of programing in Python
high school level of mathematics

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

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Probabilistic Graphical Models

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Probabilistic Graphical Models

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