| Reasoning about Large Populations with Lifted Probabilistic Inference (2008) | |||||||||||||||
Abstract | |||||||||||||||
| We use a concrete problem in the context of planning meetings to show how lifted probabilistic inference can dramatically speed up reasoning. We also extend lifted inference to deal with cardinality potentials, and examine how to deal with background knowledge about a social network. Lifted inference: An example. Suppose that n people (say, n = 100) have been invited to a NIPS workshop, and we are wondering whether the attendees will overflow the 40-seat room we have reserved. A graphical model for this scenario is shown in Fig. 1(a). In this simple model, the attendance variables attend(pi) for each person pi are conditionally independent given the workshop’s popularity. We get noisy information about each person’s attendance based on the reply they have sent us: “yes”, “no”, or “no reply”. Assume for the moment that we just want to estimate the workshop’s popularity (ignoring the roomOverflow variable for now). In this case, we need to compute the marginal distribution for the popularity random variable given the reply variables. One commonly used algorithm, variable elimination (VE), computes this marginal by eliminating each attend(pi) variable | |||||||||||||||
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