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

Selection of dates

Do you have a question?

+420 731 175 867 edu@edutrainings.cz

Professional
and certified lecturers

Internationally
recognized certifications

Wide range of technical
and soft skills courses

View soft skills courses

Great customer
service

Making courses
exactly to measure your needs

Course dates
Course structure
Prerequisites

Course dates

Starting date: Upon request

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 210 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		In-person/Virtual	1 day	en/cz	210 EUR	Register
G	Guaranteed course

Didn't find a suitable date?

Write to us about listing an alternative tailor-made date.

Contact

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