4 citations · 15 across the 10 of their papers we have counts for
4 papers · 1 filter
Revisiting Algebra and Complexity of Inference in Graphical Models
Siamak Ravanbakhsh, Russell Greiner
This paper studies the form and complexity of inference in graphical models using the abstraction offered by algebraic structures. In particular, we broadly formalize inference pro…
A Generalized Loop Correction Method for Approximate Inference in Graphical Models
Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner
Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but c…
Speeding Up Planning in Markov Decision Processes via Automatically Constructed Abstractions
Alejandro Isaza, Csaba Szepesvari, Vadim Bulitko +1
In this paper, we consider planning in stochastic shortest path (SSP) problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size problems whose state space can…
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling
Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner +1
A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional dis…