How to implement single Imputation from conditional distribution?
This is just an intuitive explanation of a group of a strategy for imputing missing data.

In practice, the distribution $P(x_{mis}|x_{obs};\mathbf{\theta})$ is unknown and can be estimated at best. The best way to estimate this probability is use-case specific. Understanding how the training data was collected can help you in estimating/defining this conditional distribution.

In practice, we often do not try to get a good estimation. Keeping things simple and assuming all features are sampled from a normal distribution might get you started. 

This is $x_{mis}$ follows $N(\mu, \sigma)$ where 


$\mu = \sum_i^N \frac{x_{obs,i}}{N}$ 
$\sigma = \sum_i^N \frac{(x_{obs,i}-\mu)^2}{N-1}$


. However, such assumptions are rarely realistic and will do guarantee good models.
See this.