A Gaussian version of the iterative proportional fitting procedure (IFP-P) was applied by Speed and Kiiveri to solve the likelihood equations in graphical Gaussian models. The calculation of the maximum likelihood estimates can be seen as the problem to find a Gaussian distribution with prescribed G
Gaussian approximations to conditional distributions for multi-Gaussian processes
β Scribed by Stein, Michael
- Publisher
- Springer
- Year
- 1987
- Tongue
- English
- Weight
- 714 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0020-5958
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π SIMILAR VOLUMES
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System identiΓΏcation for stationary Gaussian processes includes an approximation problem. Currently, the subspace algorithm for this problem enjoys much attention. This algorithm is based on a transformation of a ΓΏnite time series to canonical variable form followed by a truncation. There is no proo
Maximum likelihood estimation (MLE) of hyperparameters in Gaussian process regression as well as other computational models usually and frequently requires the evaluation of the logarithm of the determinant of a positive-definite matrix (denoted by C hereafter). In general, the exact computation of