Estimating Mixture Distributions via Stochastic Mirror Descent
We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss. Framing the task as a stochastic convex optimization problem over the space of $ M $-component mixture distributions, we propose a family of estimators derived from the stochastic mirror descent (SMD) algorithm. This optimization-based approach provides a principled and flexible framework that generalizes traditional estimators and proposes a variety of novel estimators through the choice of Bregman divergences. A key advantage of our method is that it scales efficiently with the number of candidate components $ f_i $; that is, one can emplo...
| Authors | Mohammadreza Ahmadypour, Tara Javidi, Farinaz Koushanfar |
|---|---|
| Primary category | stat.ML |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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