基于WinBugs / JAGS中的if - else条件选择不同的分布

问题描述

我正在尝试编写一个Winbugs / Jags模型来建模多粒度主题模型（正是本文 - > ）

I am trying to write a Winbugs/Jags model for modeling multi grain topic models (exactly this paper -> http://www.ryanmcd.com/papers/mg_lda.pdf)

这里我想根据特定值选择不同的发行版。
例如：我想做类似的事情

Here I would like to choose a different distribution based on a particular value.For Eg: I would like to do something like

`if ( X[i] > 0.5 )
{
Z[i] ~ dcat(theta-gl[D[i], 1:K-gl])
W[i] ~ dcat(phi-gl[z[i], 1:V])
}
else
{
Z[i] ~ dcat(theta-loc[D[i], 1:K-loc])
W[i] ~ dcat(phi-loc[z[i], 1:V])
}
`

这可以在Winbugs / JAGS中完成吗？

Is this possible to be done in Winbugs/JAGS?

推荐答案

Winbugs / JAGS不是一种过程语言，所以你不能使用这样的结构。使用步骤功能。从手册中引用：

Winbugs/JAGS is not a procedural language, so you cannot use the construct like that. Use step function. Quote from the manual:

所以你需要一个技巧来改变条件：

So you need a trick to change the condition:

X[i] > 0.5   <=>
X[i] - 0.5 > 0  <=>
!(X[i] - 0.5 <= 0) <=>
!(-(X[i] - 0.5) >= 0) <=>
!(step(-(X[i] - 0.5)) == 1) <=>
step(-(X[i] - 0.5)) == 0

然后用它来索引技巧：

# then branch
Z_branch[i, 1] ~ dcat(theta-gl[D[i], 1:K-gl])
W_branch[i, 1] ~ dcat(phi-gl[z[i], 1:V])

# else branch
Z_branch[i, 2] ~ dcat(theta-loc[D[i], 1:K-loc])
W_branch[i, 2] ~ dcat(phi-loc[z[i], 1:V])

# decision here
if_branch[i] <- 1 + step(-(X[i] - 0.5)) # 1 for "then" branch, 2 for "else" branch
Z[i] ~ Z_branch[i, if_branch[i]]
W[i] ~ W_branch[i, if_branch[i]]

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