继续PRML
第二章：
Binary Variables:
- Bernouli Distribution, binomial distribution
- conjugate prior –> beta distribution
Multinomial Variables:
- multinomial distribution
- conjugate prior –> Dirichlet distribution
The Gaussian Distribution:
- univariate, multivariate, shape, limit
- conditional Gaussian, Marginal Gaussian, Bayes’ theorem
- Maximum likelihood, sequential estimation
- conjugate prior –> unknown mean, unknown variance, and both
- Mixtures of Gaussians
The exponential Family:
- Bernoulli distribution –> logistic sigmoid function
- Multinomilal distribution –> softmax function
- conjugate priors
Nonparametric Methods:
- histogram approach: p(i) = n(i) / (N * width of bin)
- p(x) = K/NV: fix V and find K –> Kernel Estimator . fix K and find V –> K-nearest-nerghbour estimator
- Kernel Estimator: estimate new data x according to old datas in V
- K-nearest-nerghbour estimator: estimate new data x according to neighbors within K
import views:
1. posterior = prior * ML
2. conjugate prior
3. sequential model to deal with large dataset(update data with disgarding the old data)
4. Gaussian Distribution and its variation
5. nonparametic method
6. hyperparameter: to model the distribution of parameter