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model{
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# data likelihood
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for (t in 1:N){
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log(mu[t]) <- b0 + b1*step(t-tau)
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D[t] ~ dpois(mu[t])
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y[t] ~ dpois(mu[t])
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}
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# x ∼ dnorm(mu,tau) tau=1/sigma^2
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b0 ~ dnorm(0,1)
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b1 ~ dnorm(0,1)
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tau ~ dunif(1, N)
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}

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