Bayesian Statistics: Mixture Models
Completed by Clément Pierre-André Jacquet
October 13, 2024
21 hours (approximately)
Clément Pierre-André Jacquet's account is verified. Coursera certifies their successful completion of Bayesian Statistics: Mixture Models
What you will learn
Explain the basic principles behind the algorithm for fitting a mixture model.
Compute the expectation and variance of a mixture distribution.
Use mixture models to solve classification and clustering problems, and to provide density estimates.
Skills you will gain
- Category: Probability Distribution
- Category: Statistical Inference
- Category: Statistical Software
- Category: Sampling (Statistics)
- Category: Statistical Modeling
- Category: Bayesian Statistics
- Category: Classification Algorithms
- Category: R Programming
- Category: R (Software)
- Category: Markov Model
- Category: Simulations
- Category: Mathematical Modeling
