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Here, [math]\tilde{X}[/math] and [math]\tilde{Y}[/math] respectively represent the dependent and dependent variables after intervening [math]X[/math] into a uniform distribution (i.e. prior distribution), while keeping the causal mechanism [math]f[/math] unchanged. It is worth noting that in the literature<ref name='tononi_2008'/>, the author did not explicitly provide the form of the [[KL Divergence]]. In subsequent literature (<ref name='IIT3.0'>{{cite journal|author1=Oizumi M|author2=Albantakis L|author3=Tononi G|year=2014|title=From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0|journal=PLoS Computational Biology|volume=10|number=5|page=e1003588}}</ref>), the author used other symmetry measures related to probability distribution distance, such as the [[Bulldozing Distance]].

Here, [math]\tilde{X}[/math] and [math]\tilde{Y}[/math] respectively represent the dependent and dependent variables after intervening [math]X[/math] into a uniform distribution (i.e. prior distribution), while keeping the causal mechanism [math]f[/math] unchanged. It is worth noting that in the literature<ref name='tononi_2008'/>, the author did not explicitly provide the form of the [[KL Divergence]]. In subsequent literature (Integrated Information Theory 3.0 Version<ref name='IIT3.0'>{{cite journal|author1=Oizumi M|author2=Albantakis L|author3=Tononi G|year=2014|title=From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0|journal=PLoS Computational Biology|volume=10|number=5|page=e1003588}}</ref>), the author used other symmetry measures related to probability distribution distance, such as the [[Bulldozing Distance]].

In fact, [math]ei(f,Y_0)[/math] is the effective information value under a certain [math]Y_0[/math] value. If we take the average of all [math]Y_0[/math] values, we can obtain the effective information in the usual sense, which is the {{EquationNote|1}} equation. To understand this, we first need to introduce the [[Bayesian Formula]], which is:

In fact, [math]ei(f,Y_0)[/math] is the effective information value under a certain [math]Y_0[/math] value. If we take the average of all [math]Y_0[/math] values, we can obtain the effective information in the usual sense, which is the equation {{EquationNote|1}}. To understand this, we first need to introduce the [[Bayesian Formula]], which is:

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