Scholarlink

<p>We propose a new Markov chain Monte Carlo method to sample from truncated multivariate Gaussian (TMG) distributions under linear inequality constraints. Unlike existing approaches, our method is applicable even when the underlying, unconstrained Gaussian distribution is improper, and whatever the number of constraints. Our algorithm relies on continuous Gaussian mixture decompositions of the target TMG distribution, derived from novel integral identities. Such decompositions are exact within the admissible domain, which allows us to get (asymptotically) exact samples from the TMG using blocked Gibbs updates followed by a rejection step to discard samples outside the admissible domain. Empirical results demonstrate that the proposed method outperforms state-of-the-art alternatives over a set of challenging settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A continuous gaussian mixture approach to sample multivariate gaussians constrained by linear inequalities

  • 期刊论文

  • 发表日期：

    2026年7月9日

查看全文
  • Mehdi Amrouche,
  • Jérôme Idier,
  • Hervé Carfantan

摘要

We propose a new Markov chain Monte Carlo method to sample from truncated multivariate Gaussian (TMG) distributions under linear inequality constraints. Unlike existing approaches, our method is applicable even when the underlying, unconstrained Gaussian distribution is improper, and whatever the number of constraints. Our algorithm relies on continuous Gaussian mixture decompositions of the target TMG distribution, derived from novel integral identities. Such decompositions are exact within the admissible domain, which allows us to get (asymptotically) exact samples from the TMG using blocked Gibbs updates followed by a rejection step to discard samples outside the admissible domain. Empirical results demonstrate that the proposed method outperforms state-of-the-art alternatives over a set of challenging settings.