Cross Validated Asked on December 11, 2021
In the book Bayesian Data Analysis 3rd edition by Gelman et al., the solution for task 3.15 on time series data involves the following expression:
$$p(y_t|y_{t-1})p(y_{t+1}|y_t) propto p(y_t|y_{t-1}, y_{t+1})$$
This means that $y_t$ depends only on $y_{t-1}$ and $y_{t+1}$. But how do I derive the right-hand side?
Use the defintion of conditional probability to get this formula:
$$ begin{align} P(y_{t+1}, y_t, y_{t-1}) &= P(y_{t+1}|y_t, y_{t-1}) cdot P(y_t, y_{t-1})\ &=P(y_{t+1}|y_t, y_{t-1}) cdot P(y_t| y_{t-1})cdot P(y_{t-1})\ end{align} $$
If you assume Markove process, you have $P(y_{t+1}|y_t, y_{t-1}) = P(y_{t+1}|y_t)$.
Plugging the formula, we get $$ P(y_{t+1}, y_t, y_{t-1}) =P(y_{t+1}|y_t) cdot P(y_t| y_{t-1})cdot P(y_{t-1})\ $$
You can expand the original probability $P(y_{t+1}, y_t, y_{t-1})$ in this way also: $$ begin{align} P(y_{t+1}, y_t, y_{t-1}) &= P(y_{t}|y_{t+1}, y_{t-1}) cdot P(y_{t+1}, y_{t-1})\ &=P(y_{t}|y_{t+1}, y_{t-1}) cdot P(y_{t+1}| y_{t-1})cdot P(y_{t-1})\ end{align} $$
Under a Markove process hypothesis, $y_{t+1}$ doesn't depend on $y_{t-1}$. Therefore, we have $P(y_{t+1}| y_{t-1}) = P(y_{t+1})$.
Equating two equations, we have $$ P(y_{t+1}|y_t) cdot P(y_t| y_{t-1})cdot P(y_{t-1}) = P(y_{t}|y_{t+1}, y_{t-1}) cdot P(y_{t+1})cdot P(y_{t-1}) $$
Cancelling the term $P(y_{t-1})$, we arrive at this equation:
$$ P(y_{t+1}|y_t) cdot P(y_t| y_{t-1}) = P(y_{t}|y_{t+1}, y_{t-1}) cdot P(y_{t+1}) $$
Answered by hbadger19042 on December 11, 2021
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