论文标题
ABC-DI：离散数据的近似贝叶斯计算
ABC-Di: Approximate Bayesian Computation for Discrete Data
论文作者
论文摘要
许多现实生活中的问题表示为黑框，即，内部工作是无法访问的，或者无法定义了可能性函数的封闭形式的数学表达。对于连续的随机变量，可以通过近似贝叶斯计算（ABC）的一组方法来解决无可能的推理问题。但是，对于离散随机变量的类似方法尚待制定。在这里，我们旨在填补这一研究空白。我们建议使用基于人群的MCMC ABC框架。此外，我们提出了有效的马尔可夫内核，并提出了一个受差异进化启发的新内核。我们评估了有关已知似然函数问题的提议方法，即发现基于QMR-DT网络的潜在疾病，以及三个无可能的无可能推理问题：（i）具有未知的可能性函数的QMR-DT网络，（II）学习二进制神经网络，以及（iii）Neural Architectect搜索。获得的结果表明，提出的框架的高潜力和新的马尔可夫内核的优越性。
Many real-life problems are represented as a black-box, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables likelihood-free inference problems can be solved by a group of methods under the name of Approximate Bayesian Computation (ABC). However, a similar approach for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose to use a population-based MCMC ABC framework. Further, we present a valid Markov kernel, and propose a new kernel that is inspired by Differential Evolution. We assess the proposed approach on a problem with the known likelihood function, namely, discovering the underlying diseases based on a QMR-DT Network, and three likelihood-free inference problems: (i) the QMR-DT Network with the unknown likelihood function, (ii) learning binary neural network, and (iii) Neural Architecture Search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.
