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I'm trying to implement the GP with a Poisson likelihood whose parameter may vary with the observations1:
$$ y_i \sim Poisson(e_i exp(f_i))$$
where f_i is the GP. Is this possible?
I'm trying to implement the GP with a Poisson likelihood whose parameter may vary with the observations1:
$$ y_i \sim Poisson(e_i exp(f_i))$$
where f_i is the GP. Is this possible?
Footnotes
Approximate inference for disease mapping with sparse Gaussian processes, Jarno Vanhatalo, Ville Pietiläinen, Aki Vehtari (Eq. 1) ↩
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