# Find normalization constant from factorized density

Please consider the following Bayes Network:

We can express density $$p(\mathbf{x}_1 | \mathbf{x}_0, \mathbf{y}_1)$$ in terms of measurement and motion models by ignoring normalization constants as:

\begin{align}p(\mathbf{x}_1 | \mathbf{x}_0, \mathbf{y}_1) \propto &\ p(\mathbf{x}_1, \mathbf{x}_0, \mathbf{y}_1) \\ =&\ p(\mathbf{x}_1 | \mathbf{x}_0) p(\mathbf{y}_1 | \mathbf{x}_1) \end{align}

Since $$x_0$$ is assumed to be known, thus the prior, $$p(x_0)$$, would evaluate to a constant and we ignored it in factorised density.

My question is how we can find the $$p(x_0)$$ from the expression above?