13,217 research outputs found
Arc Reversals in Hybrid Bayesian Networks with Deterministic Variables
This article discusses arc reversals in hybrid Bayesian networks with deterministic variables. Hybrid Bayesian networks contain a mix of discrete and continuous chance variables. In a Bayesian network representation, a continuous chance variable is said to be deterministic if its conditional distributions have zero variances. Arc reversals are used in making inferences in hybrid Bayesian networks and influence diagrams. We describe a framework consisting of potentials and some operations on potentials that allows us to describe arc reversals between all possible kinds of pairs of variables. We describe a new type of conditional distribution function, called partially deterministic, if some of the conditional distributions have zero variances and some have positive variances, and show how it can arise from arc reversals
Two dimensional outflows for cellular automata with shuffle updates
In this paper, we explore the two-dimensional behavior of cellular automata
with shuffle updates. As a test case, we consider the evacuation of a square
room by pedestrians modeled by a cellular automaton model with a static floor
field. Shuffle updates are characterized by a variable associated to each
particle and called phase, that can be interpreted as the phase in the step
cycle in the frame of pedestrian flows. Here we also introduce a dynamics for
these phases, in order to modify the properties of the model. We investigate in
particular the crossover between low- and high-density regimes that occurs when
the density of pedestrians increases, the dependency of the outflow in the
strength of the floor field, and the shape of the queue in front of the exit.
Eventually we discuss the relevance of these results for pedestrians.Comment: 20 pages, 5 figures. v2: 16 pages, 5 figures; changed the title,
abstract and structure of the paper. v3: minor change
Hybrid stochastic kinetic description of two-dimensional traffic dynamics
In this work we present a two-dimensional kinetic traffic model which takes
into account speed changes both when vehicles interact along the road lanes and
when they change lane. Assuming that lane changes are less frequent than
interactions along the same lane and considering that their mathematical
description can be done up to some uncertainty in the model parameters, we
derive a hybrid stochastic Fokker-Planck-Boltzmann equation in the
quasi-invariant interaction limit. By means of suitable numerical methods,
precisely structure preserving and direct Monte Carlo schemes, we use this
equation to compute theoretical speed-density diagrams of traffic both along
and across the lanes, including estimates of the data dispersion, and validate
them against real data
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