Approximate Inference
Context
- Approximate Inference is a backup method for performing inference if creating a Junction Tree for Exact Inference is prohibitive in terms of memory requirements or computation time.
- Approximate Inference is based on the law of large numbers and uses network simulation to approximate the probabilities.
- While Approximate Inference initially requires very little memory compared to Exact Inference, each inference step with Approximate Inference takes time to perform.
Usage
- You can select Approximate Inference by choosing
Main Menu > Inference > Approximate Inference.
Bear in mind that most of BayesiaLab’s analysis tools and functions require Exact Inference. They will not work with Approximate Inference.