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- Sampath Srinivas
- UAI
- 1993

The Noisy-Or model is convenient for de scribing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to ar bitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for… (More)

- Sampath Srinivas
- UAI
- 1994

Model-based diagnosis reasons backwards from a functional schematic of a system to isolate faults given observations of anoma lous behavior. We develop a fully proba bilistic approach to model based diagno sis and extend it to support hierarchical models. Our scheme translates the func tional schematic into a Bayesian network and diagnostic inference… (More)

An algorithm for automated construction of a sparse Bayesian network given an unstructured probabilis tic model and causal domain information from an expert has been developed and implemented. The goal is to obtain a network that explicitly reveals as much information regarding conditional indepen dence as possible. The network is built incrementally… (More)

Model-based diagnosis centers on the use of a behavioral model of a system to infer diagnoses of anomalous behavior. For model-based diagnosis techniques to become practical, some serious problems in the modeling of uncertainty and in the tractability of uncertainty management have to be addressed. These questions include: How can we tractably generate… (More)

- Sampath Srinivas
- UAI
- 1995

The goal of diagnosis is to compute good re pair strategies in response to anomalous sys tem behavior. In a decision theoretic frame work, a good repair strategy has low expected cost. In a general formulation of the problem, the computation of the optimal (lowest ex pected cost) repair strategy for a system with multiple faults is intractable. In this… (More)

- Sampath Srinivas, P. Pandurang Nayak
- UAI
- 1996

Over the past several years Bayesian net works have been applied to a wide variety of problems. A central problem in applying Bayesian networks is that of finding one or more of the most probable instantiations of a network. In this paper we develop an efficient algorithm that incrementally enumerates the instantiations of a Bayesian network in de… (More)

We describe a collaborative research and development effort between the Palo Alto Laboratory of the Rockwell Science Center, Rockwell Space Operations Company, and the Propulsion Systems Section of NASA Johnson Space Center to design computational tools that can manage the complexity of information displayed to human operators in high-stakes, time-critical… (More)

- Sampath Srinivas, Eric Horvitz
- UAI
- 1995

The goal of model based diagnosis is to iso late causes of anomalous system behavior and recommend cost e ective repair actions In general precomputing optimal repair policies is intractable To date investigators address ing this problem have explored approxima tions that either impose restrictions on the system model such as a single fault assump tion or… (More)

- Juan Lang, Alexei Czeskis, Dirk Balfanz, Marius Schilder, Sampath Srinivas
- Financial Cryptography
- 2016

“Security Keys” are second-factor devices that protect users against phishing and man-in-the-middle attacks. Users carry a single device and can self-register it with any online service that supports the protocol. The devices are simple to implement and deploy, simple to use, privacy preserving, and secure against strong attackers. We have shipped support… (More)

- Sampath Srinivas, John S. Breese
- ArXiv
- 2011

IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software environment for creation and evaluation of belief networks and influence diagrams. IDEAL is primarily a research tool and provides an implementation of many of the latest developments in belief network and influence diagram evaluation in a unified framework. This paper describes IDEAL… (More)