Bayesian decision making (DM) quantifies information by the probability density (pd) of treated variables. Gradual accumulation of information during acting increases the DM quality reachable by an agent exploiting it. The inspected accumulation way uses a parametric model forecasting observable DM outcomes and updates the posterior pd of its unknown parameter. In the thought multi-agent case, a neighbouring agent, moreover, provides a privately-designed pd forecasting the same observation. This pd may notably enrich the information of the focal agent. Bayes’ rule is a unique deductive tool for a lossless compression of the information brought by the observations. It does not suit to processing of the forecasting pd. The paper extends solutions of this case. It: \(\triangleright\) refines the Bayes’-rule-like use of the neighbour’s forecasting pd \(\triangleright\) deductively complements former solutions so that the learnable neighbour’s reliability can be taken into account \(\triangleright\) specialises the result to the exponential family, which shows the high potential of this information processing \(\triangleright\) cares about exploiting population statistics.

贝叶斯决策 (DM) 通过处理变量的概率密度 (pd) 量化信息。在行动过程中信息的逐渐积累增加了利用它的代理可达到的 DM 质量。检查累积方式使用参数模型预测可观察的 DM 结果并更新其未知参数的后验 pd。此外，在思想多代理的情况下，相邻代理提供了一个私人设计的 pd 预测相同的观察。这个 pd 可以显着丰富焦点代理的信息。贝叶斯规则是一种独特的演绎工具，用于对观测带来的信息进行无损压缩。它不适合预测 pd 的处理。本文扩展了该案例的解决方案。它：\(\triangleright\) 改进了贝叶斯规则式使用邻居的预测 pd \(\triangleright\) 演绎补充以前的解决方案，以便可以考虑可学习邻居的可靠性\(\triangleright\) 将结果专门化为指数族，其中显示了这种信息处理的巨大潜力\(\triangleright\) 关心利用人口统计数据。