- R Programming
- Probability Distribution
- Machine Learning Methods
- Unsupervised Learning
- Statistical Modeling
- Probability & Statistics
- Markov Model
- Statistical Software
- Bayesian Statistics
- Statistical Methods
- Classification Algorithms
- Mathematical Modeling
Bayesian Statistics: Mixture Models
Completed by Rajdeep Haldar
September 30, 2020
21 hours (approximately)
Rajdeep Haldar'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
