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Exact Sampling for Regular and Markov Constraints with Belief Propagation

机译：具有信念传播的常规和马尔可夫约束的精确抽样

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Sampling random sequences from a statistical model, subject to hard constraints, is generally a difficult task. In this paper, we show that for Markov models and a set of REGULAR global constraints and unary constraints, we can perform perfect sampling. This is achieved by defining a factor graph, composed of binary factors that combine a Markov chain and an automaton. We apply a simplified version of belief propagation to sample random sequences satisfying the global constraints, with their correct probability. Since the factor graph is linear, this procedure is efficient and exact. We illustrate this approach to the generation of sequences of text or music, imitating the style of a corpus, and verifying validity constraints, such as syntax or meter.
机译：从统计模型中采样随机序列，受到硬限制，通常是一项艰巨的任务。在本文中，我们展示了为马尔可夫模型和一套常规的全局限制和一组规则约束，我们可以执行完美的采样。这是通过定义由组合马尔可夫链和自动机的二元因子组成的因子图来实现的。我们应用了一个简化的信仰传播版本，以对满足全局约束的随机序列，具有正确的概率。由于因子图是线性的，因此该过程是有效和精确的。我们说明了这种方法来生成文本或音乐的序列，模仿语料库的样式，并验证有效性约束，例如语法或仪表。

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