Ranking hypotheses to minimize the search cost in probabilistic inference models

Ranking hypotheses to minimize the search cost in probabilistic inference models

Suppose that we are given n mutually exclusive hypotheses, m mutually exclusive possible observations, the conditional probabilities for each of these observations under each hypothesis, and a method to probe each hypothesis whether it is the true one. We consider the problem of efficient searching for the true (target) hypothesis given a particular observation. Our objective is to minimize the...

Linear Programs for Hypotheses Selection in Probabilistic Inference Models

We consider an optimization problem in probabilistic inference: Given n hypotheses H j, m possible observations Ok, their conditional probabilities pk j, and a particular Ok, select a possibly small subset of hypotheses excluding the true target only with some error probability ε. After specifying the optimization goal we show that this problem can be solved through a linear program in mn varia...

Efficient Probabilistic Inference with Partial Ranking Queries

Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distributions. We approach the modeling problem from the computational principle th...

Variational Inference in Probabilistic Models

Probabilistic inference in graphical models

Jordan and Weiss: Probabilistic inference in graphical models 1 INTRODUCTION A " graphical model " is a type of probabilistic network that has roots in several different framework provides a clean mathematical formalism that has made it possible to understand the relationships among a wide variety of network-based approaches to computation, and in particular to understand many neural network al...