InhomogeneousPoissonProcess
multinomial.InhomogeneousPoissonProcess(self, alpha, rho, weights, k=100)Canary software release test from Lindon and Malek (2022).
- Parent class:
InhomogeneousBernoulliProcess - Example
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| alpha | float | Probability of Type I error \(\alpha\). | required |
| rho | np.ndarray | Assignment probabilities \(\mathbf{\rho}\). | required |
| weights | np.ndarray | Contrast weights \(W\). | required |
| k | float | Concentration for Dirichlet prior parameters \(\mathbf{\alpha}_0 = k \mathbf{\rho}\). | 100 |
References
Lindon, Michael, and Alan Malek. 2022. “Anytime-Valid Inference for Multinomial Count Data.” In Advances in Neural Information Processing Systems, edited by Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho. https://openreview.net/forum?id=a4zg0jiuVi.