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Thermodynamics and Garbage Collection
 In ACM Sigplan Notices
, 1994
"... INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over ..."
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Cited by 11 (0 self)
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INTRODUCTION Computer scientists should have a knowledge of abstract statistical thermodynamics. First, computer systems are dynamical systems, much like physical systems, and therefore an important first step in their characterization is in finding properties and parameters that are constant over time (i.e., constants of motion). Second, statistical thermodynamics successfully reduces macroscopic properties of a system to the statistical behavior of large numbers of microscopic processes. As computer systems become large assemblages of small components, an explanation of their macroscopic behavior may also be obtained as the aggregate statistical behavior of its component parts. If not, the elegance of the statistical thermodynamical approach can at least provide inspiration for new classes of models. 1 Third, the components of computer systems are approaching the same size as the microscopic pr
Analysis and Simulation of a Stochastic, DiscreteIndividual Model of STD Transmission with Partnership Concurrency
 Department of Probability and Statistics, University of Sheffield
, 2002
"... Deterministic differential equation models indicate that partnership concurrency and nonhomogeneous mixing patterns play an important role in the spread of sexuallytransmitted infections. Stochastic discreteindividual simulation studies arrive at similar conclusions, but from a very different mod ..."
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Deterministic differential equation models indicate that partnership concurrency and nonhomogeneous mixing patterns play an important role in the spread of sexuallytransmitted infections. Stochastic discreteindividual simulation studies arrive at similar conclusions, but from a very different modeling perspective. This paper presents a stochastic discreteindividual infection model that helps to unify these two approaches to infection modeling. The model allows for both partnership concurrency, as well as the infection, recovery, and reinfection of an individual from repeated contact with a partner, as occurs with many mucosal infections. The simplest form of the model is a networkvalued Markov Chain, whose nodes are individuals and arcs represent partnerships. Connections between the differential equation and discreteindividual approaches are constructed with largepopulation limits that approximate endemic levels and equilibrium probability distributions that describe partnersh...