Learning for High-Dimensional Parameter Spaces
Learning for High-Dimensional Parameter Spaces
- In some classes of models there are exact Bayesian methods for computing the posterior distribution
- Decomposable models with complete data and conjugate prior distributions
- There are many approximate methods for cases in which exact methods are unavailable
- Maximum likelihood or maximum a posteriori methods
- EM algorithm
- Mean field approximation
- Backpropagation
- Monte Carlo
- Gibbs sampling
- Metropolis-Hastings sampling
- Weighted Monte Carlo