Probabilistic Network Models

2012 ◽  
Keyword(s):  
Network Models ◽  
Probabilistic Network

Mapping resource conflicts with probabilistic network models

2016 ◽  
Vol 139 ◽  
pp. 1463-1477 ◽  
Author(s):  
Flaminia Musella ◽  
Maria Caterina Bramati ◽  
Giorgio Alleva
Keyword(s):  
Network Models ◽  
Resource Conflicts ◽  
Probabilistic Network ◽  
Mapping Resource

scholarly journals Expert Systems and Probabilistic Network Models

1997 ◽  
Author(s):  
Enrique Castillo ◽  
José Manuel Gutiérrez ◽  
Ali S. Hadi
Keyword(s):  
Expert Systems ◽  
Network Models ◽  
Probabilistic Network

New Probabilistic Network Models and Algorithms for Oncogenesis

2006 ◽  
Vol 13 (4) ◽  
pp. 853-865 ◽  
Author(s):  
Marcus Hjelm ◽  
Mattias Höglund ◽  
Jens Lagergren
Keyword(s):  
Network Models ◽  
Probabilistic Network

A Bayesian framework for modelling the preferential selection process in respondent-driven sampling

2021 ◽  
pp. 1471082X2110439
Author(s):  
Katherine R. McLaughlin
Keyword(s):  
Rational Choice ◽  
Selection Process ◽  
Network Models ◽  
Bayesian Framework ◽  
Respondent Driven Sampling ◽  
Hidden Populations ◽  
Preferential Selection ◽  
Probabilistic Network ◽  
Sampling Process ◽  
Present Simulation

In sampling designs that utilize peer recruitment, the sampling process is partially unknown and must be modelled to make inference about the population and estimate standard outcomes like prevalence. We develop a Bayesian model for the recruitment process for respondent-driven sampling (RDS), a network sampling methodology used worldwide to sample hidden populations that are not reachable by conventional sampling techniques, including those at high risk for HIV/AIDS. Current models for the RDS sampling process typically assume that recruitment occurs randomly given the population social network, but this is likely untrue in practice. To model preferential selection on covariates, we develop a sequential two-sided rational choice framework, which allows generative probabilistic network models to be created for the RDS sampling process. In the rational choice framework, members of the population make recruitment and participation choices based on observable nodal and dyadic covariates to maximize their utility given constraints. Inference is made about recruitment preferences given the observed recruitment chain in a Bayesian framework by incorporating the latent utilities and sampling from the joint posterior distribution via Markov chain Monte Carlo. We present simulation results and apply the model to an RDS study of Francophone migrants in Rabat, Morocco.

scholarly journals ProbNV: probabilistic verification of network control planes

2021 ◽  
Vol 5 (ICFP) ◽  
pp. 1-30
Author(s):  
Nick Giannarakis ◽  
Alexandra Silva ◽  
David Walker
Keyword(s):  
Type System ◽  
Network Models ◽  
Network Control ◽  
Symbolic Simulation ◽  
Probabilistic Network ◽  
Bounded Number ◽  
Wide Range ◽  
Network Topologies ◽  
Usual Representation ◽  
Network Verification

ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional programming language designed for network verification. This language comes equipped with a novel type system that characterizes the sort of representation to be used for each data structure: concrete for the usual representation of values; symbolic for a BDD-based representation of sets of values; and multi-value for an MTBDD-based representation of values that depend upon symbolics. Careful use of these varying representations speeds execution of symbolic simulation of network models. The MTBDD-based representations are also used to calculate probabilistic properties of network models once symbolic simulation is complete. We implement the language and evaluate its performance on benchmarks constructed from real network topologies and synthesized routing policies.

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

2019 ◽  
Vol 42 ◽  
Author(s):  
Hanna M. van Loo ◽  
Jan-Willem Romeijn
Keyword(s):  
Psychiatric Disorders ◽  
Network Models ◽  
Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

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