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2 edition of Large scale models and large scale problems found in the catalog.

Large scale models and large scale problems

Peter Smith

Large scale models and large scale problems

the case of the health services

by Peter Smith

  • 152 Want to read
  • 40 Currently reading

Published by York University, Centre for Health Economics in York .
Written in English


Edition Notes

Statementby Peter Smith.
SeriesHealth economics discussion paper series / York University, Centre for Health Economics -- no.110, Health economics discussion paper (York University, Centre for Health Economics) -- no.110.
ID Numbers
Open LibraryOL19559210M

  This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

MCMC methods face two central difficulties when applied to large-scale inverse problems: first, the forward models (typically in the form of partial differential equations) that map uncertain parameters to observable quantities make the evaluation of the probability density at any point in parameter space very expensive; and second, the high Cited by: This book deals with one of the fundamental problems of nonequilibrium statistical mechanics: the explanation of large-scale dynamics (evolution differential equations) from models of a very large number of interacting particles. This book addresses both researchers and students.

Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Build powerful ensembles at scale; Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine; In Detail. Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But.


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Large scale models and large scale problems by Peter Smith Download PDF EPUB FB2

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