Journal
PHYSICAL REVIEW LETTERS
Volume 107, Issue 22, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.107.220601
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Funding
- James S. McDonnell Foundation
- Canadian Institute for Advanced Research-Neural Computation and Perception
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Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function. We propose a new parameter fitting method, minimum probability flow (MPF), which is applicable to any parametric model. We demonstrate parameter estimation using MPF in two cases: a continuous state space model, and an Ising spin glass. In the latter case, MPF outperforms current techniques by at least an order of magnitude in convergence time with lower error in the recovered coupling parameters.
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