data interpolation

Learning Process Models with Missing Data

We review the task of inductive process modeling, which uses domain knowledge to compose explanatory models of continuous dynamic systems, and investigate methods for learning with missing data. Using experiments with synthetic and natural data, we compare an expectation maximization approach with one that simply ignores missing data.

Will Bridewell, Pat Langley, Steve Racunas, Stuart R. Borrett