Neural networks are commonly used to model conditional probability distributions. The idea is to represent distributional parameters as functions of conditioning events, where the function is determined by the architecture and weights of the network. An issue to be resolved is the link between distr
✦ LIBER ✦
Unconstrained parametrizations for variance-covariance matrices
✍ Scribed by José C. Pinheiro; Douglas M. Bates
- Publisher
- Springer US
- Year
- 1996
- Tongue
- English
- Weight
- 803 KB
- Volume
- 6
- Category
- Article
- ISSN
- 0960-3174
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