Feature allocation models postulate a sampling distribution whose parameters are derived from shared features. Bayesian models place a prior distribution on the feature allocation, and Markov chain Monte Carlo is typically used for model fitting, which results in thousands of feature allocations sampled from the posterior distribution. Based on these samples, we propose a method to provide a point estimate of a latent feature allocation. First, we introduce FARO loss, a function between feature allocations which satisfies quasi-metric properties and allows for comparing feature allocations with differing numbers of features. The loss involves finding the optimal feature ordering among all possible, but computational feasibility is achieved by framing this task as a linear assignment problem. We also introduce the FANGS algorithm to obtain a Bayes estimate by minimizing the Monte Carlo estimate of the posterior expected FARO loss using the available samples. FANGS can produce an estimate other than those visited in the Markov chain. We provide an investigation of existing methods and our proposed methods. Our loss function and search algorithm are implemented in the fangs package in R.
翻译:根据这些样本,我们建议了一种方法来提供潜在特征分配的点数估计。首先,我们引入了FARO损失,这是功能分配之间的一种功能性功能性功能性功能性功能性功能性功能性功能,它满足准计量特性性能,并能够比较特征性能分布的不同特性性能。损失涉及在所有可能的地方找到最佳特征性能排序,但通过将这一任务描述为线性分配问题,可以实现计算可行性。我们还引入了FANGS算法,通过利用现有样本尽量减少蒙特卡洛对预期FARO损失的蒙特卡洛估计,获取Bayes估计值。FANGS可以产生与Markov链中访问过的相异的估计值性能性能性能。我们对现有方法和拟议方法进行了调查。我们的损失函数和搜索算法在R的扇形包中得到了实施。