A linear-Gaussian model is a Bayes net where all the variables are Gaussian, and each variable's mean is linear in the values of its parents. They are widely used because they support efficient inference. Linear dynamical systems are an important special case.
This concept has the prerequisites:
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location: Section 7.2, pages 251-254
- information form for multivariate Gaussians
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 8.1.4, pages 370-371
- We can answer conditional probability queries using:learn the parameters in a linear-Gaussian model using [linear regression](linear_regression) .
- State-space models are an important example of linear-Gaussian models.
- Kalman smoothing can be [viewed](kalman_smoothing_as_bp) " as belief propagation in a linear-Gaussian model.
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