3 research outputs found
Valueâbased potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models
This study was jointly supported by the Spanish Ministry of Education and Science under projects PID2019-106758GB-C31 and TIN2016-77902-C3-2-P, and the European Regional Development Fund (FEDER). Funding for open access charge from Universidad de Granada/CBUA.When dealing with complex models (i.e., models with
many variables, a high degree of dependency between
variables, or many states per variable), the efficient representation
of quantitative information in probabilistic
graphical models (PGMs) is a challenging task. To address
this problem, this study introduces several new structures,
aptly named valueâbased potentials (VBPs), which are
based exclusively on the values. VBPs leverage repeated
values to reduce memory requirements. In the present
paper, they are compared with some common structures,
like standard tables or unidimensional arrays, and probability
trees (PT). Like VBPs, PTs are designed to reduce
the memory space, but this is achieved only if value repetitions
correspond to contextâspecific independence
patterns (i.e., repeated values are related to consecutive
indices or configurations). VBPs are devised to overcome
this limitation. The goal of this study is to analyze the
properties of VBPs. We provide a theoretical analysis of
VBPs and use them to encode the quantitative information
of a set of wellâknown Bayesian networks, measuring
the access time to their content and the computational
time required to perform some inference tasks.Spanish Government PID2019-106758GB-C31
TIN2016-77902-C3-2-PEuropean Commissio
Towards Bayesian Model-Based Demography
This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration â one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly
Towards Bayesian Model-Based Demography
This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration â one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly