[Faculty] Fwd: [CSRC.COLLOQUIUM] "What is Bayesian inference, why is it useful in Earth science and why is it challenging to do numerically?"

[Jose Castillo]

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DATE:  *Friday, April 17, 2020*



TITLE:

*What is Bayesian inference, why is it useful in Earth science and why is
it challenging to do numerically?*


TIME:  *3:00-4:00PM*




LOCATION:

*Join Zoom Meeting -  https://SDSU.zoom.us/j/99859040967
<https://SDSU.zoom.us/j/99859040967> *




SPEAKER/BIO:


*Dr. **Matthias Morzfeld, Associate Professor, Institute of Geophysics and
Planetary Physics, University of CA, San Diego*





ABSTRACT:
I will first review Bayesian inference, which means to incorporate
information from observations (data) into a numerical model, and will give
some examples of applications in Earth science. The numerical solution of
Bayesian inference problems is often based on sampling a posterior
probability distribution. Sampling posterior distributions is difficult
because these are usually high-dimensional (many parameters or states to
estimate) and non-standard (e.g., not Gaussian). In particular a
high-dimension causes numerical difficulties and slow convergence in many
sampling algorithms. I will explain how ideas from numerical weather
prediction can be leveraged to design Markov chain Monte Carlo (MCMC)
samplers whose convergence rates are independent of the problem dimension
for a well-defined class of problems.



Host: Samuel Shen

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