In the left panel, η ∈ ∆(∆(Ω)) has support in the green region. Because η is Bayes plausible, Eη[s¯(ω)] � µ∗ (ω) for each ω ∈ Ω. In the middle panel, we separate η into η0 and η1, where ηa has support in Pa. In the right panel, we depict m0 and m1, where Eηa [s¯(ω)] � m∗ a(ω) and ma ∈ Conv(Pa). Finally, ba is defined as Pη(a(s¯) � a).
时间: 2024-04-05 15:34:22 浏览: 17
在左侧面板中,η∈∆(∆(Ω))支持绿色区域。由于η是Bayes plausible的,对于每个ω∈Ω,Eη[s¯(ω)] � µ∗ (ω)。在中间面板中,我们将η分成η0和η1,其中ηa在Pa中有支持。在右侧面板中,我们描绘了m0和m1,其中Eηa[s¯(ω)] � m∗ a(ω),并且ma∈Conv(Pa)。最后,ba被定义为Pη(a(s¯) ∣ a)。
相关问题
The interpretation of the quantities ma and ba is as follows. Given a Bayes-plausible measure η, the quantity ba denotes the probability that the receiver plays action a ∈ A under the optimal strategy a(·) when the sender uses the signaling scheme corresponding to η; in other words, ba � Pη(a(s¯) � a). Similarly, ma denotes the distribution of the state ω¯ , conditioned on the receiver choosing action a. By iterated expectation, we obtain ma(ω) � Eη[s¯(ω)|a(s¯) � a]. Thus, for any a ∈ A, the quantity ma denotes the mean of all posterior beliefs the receiver holds, conditioned on choosing action a. For this reason, we refer to ma as the mean posterior of the receiver corresponding to action a ∈ A. Note that ma may not correspond to any actual posterior that the receiver holds when choosing action a ∈ A; in fact, the mean posterior ma ∈ Conv(Pa) may not even lie in the set Pa. Figure 2 gives some geometric intuition for 翻译
这些量ma和ba的解释如下。给定一个Bayes-plausible度量η,量ba表示当发送者使用与η对应的信令方案时,接收者在最优策略a(·)下选择行动a∈A的概率;换句话说,ba表示Pη(a(s¯)∣a)。类似地,ma表示在接收者选择行动a的条件下,状态ω¯的分布。通过迭代期望,我们得到ma(ω) = Eη[s¯(ω)∣a(s¯) = a]。因此,对于任何a∈A,ma表示接收者持有的所有后验信念的平均值,条件是选择行动a。出于这个原因,我们将ma称为与行动a∈A相应的接收者的平均后验值。请注意,ma可能不对应接收者在选择行动a∈A时实际持有的任何后验值;实际上,平均后验值ma∈Conv(Pa)甚至可能不在集合Pa中。图2给出了一些几何直觉。
inference in Bayes
In Bayesian inference, we start with a prior probability distribution that represents our beliefs about the probability of different events or parameters before we observe any data. We then update this prior distribution with the observed data using Bayes' theorem to obtain a posterior distribution that reflects our updated beliefs about the probability of events or parameters given the data.
The posterior distribution is obtained by multiplying the prior distribution by the likelihood of the observed data given the events or parameters, and then normalizing to obtain a probability distribution. This allows us to make probabilistic inferences about the events or parameters, such as estimating their means or variances, or making predictions about future outcomes.
Bayesian inference is a powerful framework for making inferences in a wide range of applications, including machine learning, statistics, and decision making. It allows us to incorporate prior knowledge and uncertainty into our models, and update our beliefs as new data becomes available.