Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
We study the problem of estimating multiple discrete unimodal distributions, motivated by search behavior analysis on a real-world platform. To incorporate prior knowledge of precedence relations among distributions, we impose stochastic order constraints and formulate the estimation task as a mixed-integer convex quadratic optimization problem. Experiments on both synthetic and real datasets show that the proposed method reduces the Jensen-Shannon divergence by 2.2% on average (up to 6.3%) when the sample size is small, while performing comparably to existing methods when sufficient data are available.
| Authors | Yasuhiro Yoshida, Noriyoshi Sukegawa, Jiro Iwanaga |
|---|---|
| Primary category | math.OC |
| Published | 12 March 2026 |
| Last updated at source | 5 August 2026 |
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