MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation

• URL: http://arxiv.org/abs/2306.04120v2
• Date: Sat, 10 Feb 2024 05:33:21 GMT
• Title: MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
• Authors: Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi, Nicolas G. Hadjiconstantinou
• Abstract summary: MESSY estimation is a Maximum-Entropy based Gradient and Symbolic densitY estimation method. We construct a gradient-based drift-diffusion process that connects samples of the unknown distribution function to a guess symbolic expression. We find that the addition of a symbolic search for basis functions improves the accuracy of the estimation at a reasonable additional computational cost.
• Score: 4.014524824655106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic densitY estimation method. The proposed approach recovers probability density functions symbolically from samples using moments of a Gradient flow in which the ansatz serves as the driving force. In particular, we construct a gradient-based drift-diffusion process that connects samples of the unknown distribution function to a guess symbolic expression. We then show that when the guess distribution has the maximum entropy form, the parameters of this distribution can be found efficiently by solving a linear system of equations constructed using the moments of the provided samples. Furthermore, we use Symbolic regression to explore the space of smooth functions and find optimal basis functions for the exponent of the maximum entropy functional leading to good conditioning. The cost of the proposed method for each set of selected basis functions is linear with the number of samples and quadratic with the number of basis functions. However, the underlying acceptance/rejection procedure for finding optimal and well-conditioned bases adds to the computational cost. We validate the proposed MESSY estimation method against other benchmark methods for the case of a bi-modal and a discontinuous density, as well as a density at the limit of physical realizability. We find that the addition of a symbolic search for basis functions improves the accuracy of the estimation at a reasonable additional computational cost. Our results suggest that the proposed method outperforms existing density recovery methods in the limit of a small to moderate number of samples by providing a low-bias and tractable symbolic description of the unknown density at a reasonable computational cost.

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