这是一份贝叶斯统计推断作业代写的成功案

$$
\mu_{t}=\sum_{i} \beta_{i} x_{i t}
$$
However, this yields a conditional model of the form
$$
\mu_{t \mid t-1}=\rho\left(y_{t-1}-\sum_{i} \beta_{i} x_{i, t-1}\right)+\sum_{i} \beta_{i} x_{i t}
$$
This may also be written
$$
\mu_{t \mid t-1}-\sum_{i} \beta_{i} x_{i t}=\rho\left(y_{t-1}-\sum_{i} \beta_{i} x_{i, t-1}\right)
$$

STA 144/STAT 451/STAT 506/STA 317 COURSE NOTES ：

where, again, $K$ is the unknown asymptotic maximum value. And again, we can obtain a linear structure, this time for a complementary log log link:
$$
\log \left[-\log \left(\frac{K-y}{K}\right)\right]=\log (\alpha)+\beta t
$$
We can use the same iterative procedure as before.