Say we have a Beta-Bernoulli model where $X_i$ are i.i.d. Bernoulli variables, $p(X_i=1)=\theta$, and $\theta\sim\operatorname{Beta}(\alpha,\beta)$.
The marginal likelihood is defined as
$$ p(X)=\int p(X|\theta)p(\theta)d\theta $$
Consider a concrete $X=(1,1)$.
One way to compute $p(X)$, described in "Machine Learning. A Probabilistic Perspective" by Kevin P. Murphy, is
$$p(X)=p(X_1)p(X_2|X_1)=\frac{\alpha}{\alpha+\beta}\cdot\frac{\alpha+1}{\alpha+\beta+1}$$
On the other hand, since $X_i$ are independent, we should be able to say that $P(X_2|X_1)=p(X_2)$,
$$p(X)=p(X_1)p(X_2|X_1)=\left(\frac{\alpha}{\alpha+\beta}\right)^2$$
I assume that Kevin's interpretation is the correct one, but does it mean I can no longer apply the familiar facts from the probability theory such as $p(X_2|X_1)=p(X_2)$ for independent variables?