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Latex fixes
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src/header.tex

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\usepackage{mathpazo}
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\usepackage[scale=0.9]{sourcecodepro}
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\usepackage{amssymb}
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\usepackage{mathtools}
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\linespread{1.03}
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\usepackage{titlesec}

src/stan-users-guide/finite-mixtures.qmd

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As with other event probabilities, this can be calculated in the
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generated quantities block either by sampling $z_i$ and $z_j$ and
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using the indicator function on their equality, or by computing the
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term inside the integral as a generated quantity. As with posterior
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term inside the integral as a generated quantity. As with posterior
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predictive distribute, working in expectation is more statistically
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efficient than sampling.
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@@ -575,12 +575,12 @@ The hurdle model is similar to the zero-inflated model, but more
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flexible in that the zero outcomes can be deflated as well as
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inflated. Given the probability $\theta$ and the intensity $\lambda$,
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the distribution for $y_n$ can be written as
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$$
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\[
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\begin{align*}
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y_n & = 0 \quad\text{with probability } \theta, \text{ and}\\
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y_n & \sim \textsf{Poisson}_{x\neq 0}(y_n \mid \lambda) \quad\text{with probability } 1-\theta,
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\end{align*}
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$$
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\]
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Where $\textsf{Poisson}_{x\neq 0}$ is a truncated Poisson distribution, truncated at $0$.
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The corresponding likelihood function for the hurdle model is

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