Sampling-based approximate methods

In the previous sections, we discussed a class of approximate methods that used factor manipulation methods to answer approximate queries on the models. Now, in this section, we will be discussing a very different approach to approximate inference. In this method, we will try to estimate the original distribution by instantiating all the variables or a few variables of the network. Using these instantiations, we will try to answer queries on the model. The methods using instantiations are generally known as particle-based methods, and each instantiation is known as a particle.

There are many variations of the way we select particles or create instantiations of the variables. For example, we can either create particles using a deterministic process, or we can sample particles from some distribution. Also, we can have different notions of a particle. For example, we can have a full assignment of all the variables in the network, commonly known as full particles...