# Maximum a Posteriori Estimation by Search in Probabilistic Programs

arXiv: https://arxiv.org/abs/1504.06848 · HTML: https://mlsanity.com/paper/1504.06848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2015.

## Built on by (most cited first, 1 of 1)

- [Probably Approximately Correct Maximum A Posteriori Inference](https://mlsanity.com/paper/2601.16083/) (2026)

## Abstract (arXiv)

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of random variables and dependencies. We compare BaMC to other MAP estimation algorithms and show that BaMC is faster and more robust on a range of probabilistic models.