A Probabilistic Perspective on Nonlinear Model Inversion and Data Assimilation

  1. David W. Hyndman,
  2. Frederick D. Day-Lewis and
  3. Kamini Singha
  1. Dennis Mclaughlin

Published Online: 18 MAR 2013

DOI: 10.1029/171GM17

Subsurface Hydrology: Data Integration for Properties and Processes

Subsurface Hydrology: Data Integration for Properties and Processes

How to Cite

Mclaughlin, D. (2007) A Probabilistic Perspective on Nonlinear Model Inversion and Data Assimilation, in Subsurface Hydrology: Data Integration for Properties and Processes (eds D. W. Hyndman, F. D. Day-Lewis and K. Singha), American Geophysical Union, Washington, D. C.. doi: 10.1029/171GM17

Author Information

  1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology Cambridge, Massachusetts, USA

Publication History

  1. Published Online: 18 MAR 2013
  2. Published Print: 1 JAN 2007

ISBN Information

Print ISBN: 9780875904375

Online ISBN: 9781118666463

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Keywords:

  • Groundwater flow—Mathematical models

Summary

This chapter contains sections titled:

  • Introduction

  • Deterministic Inversion

  • Bayesian Estimation

  • Variational Methods

  • Ensemble State Estimation

  • Conclusions