Abstract

From causal theory it is known that the independencies entailed by deterministic relations in a stochastic distribution cannot be represented by a faithful causal model. Deterministic relations lead to situations in which either of two variables X and Y become conditionally independent from a third variable Z by conditioning on the other variable. More generally, this occurs when X and Y contain the same information about Z, they are called information-equivalent. The joint distribution defines an equivalent partitioning of the domains of X and Y by which only the states are related for which the conditional distribution of target Z is the same, hence P (Z | X) = P (Z | Y). We propose to select the relation with the target variable containing the least complexity. Under the assumption that complexity does not increase along a Markov chain, this selection criterion results in consistent models. Faithfulness of the graph can be reestablished by limiting the conditional independencies by the simplicity criterion in cases of equivalent information. On the other hand, all conditional independencies among the variables can be retrieved from the graph by a generalized definition of the d-separation property. Finally, the PC algorithm was extended to learn models containing information-equivalent variables from data.

Causal Models for Parallel Performance Analysis

This paper proposes causal models to enhance the performance analysis of parallel processing. Causal models explicitly denote the relations among the variables involved. This makes it possible to automate the modeling task as well as to present the user a clear and understandable performance analysis. It is a flexible approach, since new environment variables can easily be integrated and performance can be estimated from incomplete knowledge. Since independency among variables is the key information, it can help the construction of a performance model that separates application and system dependency. 1

Distributed simulation of computer networks

Complexity and lack of adequate performance evaluation tools for computer networks make them analytically intractable and numerically prohibitive to evaluate. Simulation is therefore the only available evaluation method. Simulation of such complex networks is usually very slow. Using multiple processors appears to be a promising approach. In this paper, the applicability of different general purpose distributed simulation algorithms to the domain of computer network simulation is discussed. Several ways of introducing parallelism are discussed and evaluated. © 1989.SCOPUS: ar.jinfo:eu-repo/semantics/publishe