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Where the intercept $\alpha$ follows a Gaussian distribution with a very large variance, $\beta_t$ follows a second order random walk with precision $\tau_\beta$, while $\gamma_d$ a first-order random walk with precision $\tau_\gamma$. The model is then completed by INLA default prior distributions for $\phi$, $\tau_\beta$, and $\tau_\gamma$. See [`nbinomial`](https://inla.r-inla-download.org/r-inla.org/doc/likelihood/likelihood-example.pdf), [`rw1`](https://inla.r-inla-download.org/r-inla.org/doc/latent/rw1.pdf) and [`rw2`](https://inla.r-inla-download.org/r-inla.org/doc/latent/rw1.pdf) INLA help pages.
@@ -124,7 +125,10 @@ The first improvement we have done on the baseline model for nowcasting with a n
where each age class, $a$, has an intercept $\alpha_a$ following a Gaussian distribution with a very large variance, the time-age random effects, $\beta_{t,a}$, follow a joint multivariate Gaussian distribution with a separable variance components an independent Gaussian term for the age classes with precision $\tau_{a,\beta}$ and a second order random walk term for the time with precision $\tau_{\beta}$. Analogously, the delay-age random effects, $\gamma_{d,a}$, follow a joint multivariate Gaussian distribution with a separable variance components an independent Gaussian term for the age classes with precision $\tau_{a,\gamma}$ and a first order random walk term for the time with precision $\tau_{\gamma}$. The model is then completed by INLA default prior distributions for $\phi$, $\tau_{a,\beta}$, $\tau_{a,\gamma}$, $\tau_{a,\beta}$ and $\tau_\gamma$. See [`nbinomial`](https://inla.r-inla-download.org/r-inla.org/doc/likelihood/likelihood-example.pdf), [`rw1`](https://inla.r-inla-download.org/r-inla.org/doc/latent/rw1.pdf) and [`rw2`](https://inla.r-inla-download.org/r-inla.org/doc/latent/rw1.pdf) INLA help pages.
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