Most computational approaches to Bayesian experimental design require making posterior calculations, such evidence estimates, repeatedly for a large number of potential designs and/or simulated datasets. This can be expensive and prohibit scaling up these methods to models with many parameters, or designs with many unknowns to select. We introduce an efficient alternative approach without posterior calculations, based on optimising the expected trace of the Fisher information, as discussed by Walker (2016). We illustrate drawbacks of this approach, including lack of invariance to reparameterisation and encouraging designs in which one parameter combination is inferred accurately but not any others. We show these can be avoided by using an adversarial approach: the experimenter must select their design while a critic attempts to select the least favourable parameterisation. We present theoretical properties of this approach and show it can be used with gradient based optimisation methods to find designs efficiently in practice.

翻译：巴伊西亚实验设计的大多数计算方法都需要对大量潜在设计和(或)模拟数据集进行后继计算,如证据估计等。这可能是昂贵的,并禁止将这些方法推广到具有许多参数的模型,或禁止将许多未知因素加以选择的模型。我们引入了一种高效的替代方法,而无需根据沃克(Walker)(2016年)所讨论的预期渔业信息跟踪优化进行后继计算。我们举例说明了这一方法的缺点,包括缺乏重新校准的难度,鼓励了精确推算出一个参数组合但无法推断任何其他参数组合的设计。我们表明,使用对抗性方法可以避免这些方法:实验者必须选择这些方法的设计,而批评者则试图选择最不有利的参数化。我们提出了这种方法的理论属性,并表明,可以用基于梯度的优化方法来有效地进行设计。