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In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that a recursive estimate of a smooth quantile function, subject to a martingale condition, is entirely sufficient for full nonparametric Bayesian inference. We term the resulting posterior distribution as the quantile martingale posterior (QMP), which arises from an implicit generative predictive distribution. Associated with the QMP is an expedient, MCMC-free and parallelizable posterior computation scheme, which can be further accelerated with an asymptotic approximation based on a Gaussian process. Furthermore, the well-known issue of monotonicity in quantile estimation is naturally alleviated through increasing rearrangement due to the connections to the Bayesian bootstrap. Finally, the QMP has a particularly tractable form that allows for comprehensive theoretical study, which forms a main focus of the work. We demonstrate the ease of posterior computation in simulations and real data experiments.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkaf080","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T13:12:26Z","timestamp":1764767546000},"page":"1130-1159","source":"Crossref","is-referenced-by-count":3,"title":["Bayesian quantile estimation and regression with martingale posteriors"],"prefix":"10.1093","volume":"88","author":[{"given":"Edwin","family":"Fong","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science, The University of Hong Kong ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Yiu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Southampton , Southampton 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