Difference between revisions of "GeneralizedIterativeScaling"
From Cohen Courses
Jump to navigationJump to searchLine 6: | Line 6: | ||
<math> | <math> | ||
− | p_i = \pi_i \mu \prod_{s=1}^d \mu_s^{b_{si}} | + | (1) \quad \quad p_i = \pi_i \mu \prod_{s=1}^d \mu_s^{b_{si}} |
</math> | </math> | ||
Line 12: | Line 12: | ||
<math> | <math> | ||
− | \sum_{i \in I} b_{si}p_i = k_s | + | (2) \quad \quad \sum_{i \in I} b_{si}p_i = k_s |
</math> | </math> | ||
+ | |||
+ | == Existence of a solution == |
Revision as of 09:46, 27 September 2011
This is one of the earliest methods used for inference in log-linear models. Though more sophisticated and faster methods have evolved, this method provides an insight in log linear models.
What problem does it address
The objective of this method is to find a probability function of the form
satisfying the constraints