Reflections by One Statistician

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Reflections by One Statistician

• Jarrett Barber
• University of Wyoming
• Department of Statistics

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Data
Models
Assimilation Integration Fusion
Assintegrofussatamodeling.
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New Modeling Framework

• Dataprocess processparametersparameters
• Basic elements
• Fundamental probability rules conditional
  specification model locally, infer globally
• Process modeling and more empirical
  (regression) expertise
• Technical methodologies (MCMC)
• Nice thing its more plug n play
• Bad thing its more plug n play

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Issues

• A Reasonable Perspective Models (mean or
  covariance) are wrong. Check your models. (More
  than ever.)
• Model comparisons information criteria
• Observed versus predicted
• Many model components.
• How check?
• Education
• Traditional statistical methods verses
  probability modeling.
• Substantive area expertise (process modeling)
• Computational/Mathematical techniques
• Just the beginning
• Need some (new) way to facilitate modeling
  related activities
• NEON More than more data?

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Really Big Models

• When your predictions (forecasts) given by your
  best model still dont behave then use data to
  adjust states (i.e., the outputs) by optimal
  (often linear) prediction
• objective analysis (Kriging)
• KF and variants
• Adjoint method
• Often not feasible to do do inference for
  parameters inside the black box because of model
  complexity (time/computing power limitations).
  Uncertainty is a problem.
• Computer experiments carefully select a set of
  parameters at which to run the model and then
  model the model parameters to find the top of the
  hill in parameter space.

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NEON, etc.

• More data! And it will be easy to get (once
  someone figures out how to make it easy).
• Where/how do models or model components fit here?
• Do we want more than facilitated data sharing?

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Uncertainty/Variability

• Model framework that promotes explicit accounting
  of uncertainty/variability while incorporating
  information in the form of a process (or other)
  model components
• Currently seems to be favoring Bayes
• E.g., Andrew Latimer charismatic shrubs
• Priors are important for complex models to
  behave. Update the priors as we learn.

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Data and Users

• Data NEON, LTER, P2ERLS,
• Assimilators
• Mat Williams, Kelvin Droegemeier,
• Integrators
• Alan Hastings, Paul Moorcroft, Andrew Latimer,
  Jizhong (Joe) Zhu, Kiona Ogle,
• Modelers
• Forward (simulation). Inverse (inference on
  parameters).

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Models

• Embody theoretical, empirical, phenomenological,
  semi-mechanistic, mechanistic (mis)understanding
  of biological phenomenon.
• Range of understanding (model components) that go
  into such models empirical regression
  relationships (light response curves) to Big
  Science fluid flow differential equations.
• Forward modeling, parameter tuning.
• Recent (10-15 years) opportunities for more
  formal parameter estimation and
  characterization of uncertainty.

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Classic Assintegrofussatamodel
State Forecast
Model
H Operator
Initial States
Data
Adjusted States
Light Response Curves