[SciPy-User] [OT] Bayesian vs. frequentist

josef.pktd@gmai... josef.pktd@gmai...
Tue Feb 14 19:25:19 CST 2012

On Tue, Feb 14, 2012 at 4:22 PM, Phil Austin <paustin@eos.ubc.ca> wrote:
> On 12-02-14 12:39 PM, Sturla Molden wrote:
>>  On 14.02.2012 21:24, josef.pktd@gmail.com wrote:
>> > Do you expect an argument? sounds a bit like http://andrewgelman.com/
> Coincidentally, this discussion:
> http://andrewgelman.com/2012/02/adding-an-error-model-to-a-deterministic-model/
> started when a civil engineering PhD posted a request for help.  My reading
> of the ensuing discussion of both posts is that there is still a lot of
> work to
> do in bridging statistics (bayesian or frequentist) and deterministic
> modeling
> of complex systems.

I don't quite see why there should be anything deterministic (in the
sense of correctly described by a mathematical model) about the growth
of bacteria and the response of living tissue, (as there is nothing
deterministic in the behavior of the macro economy). In economics we
just add a noise variable (unexplained environmental or behavioral
shocks) everywhere.

I thought these were exactly the kind of dynamic problems that Kalman
Filter (or it's nonlinear successors) were invented for.

My main impression of the two articles and discussion is that being a
Bayesian is a lot of work if you need to have a fully specified prior
and likelihood, instead of just working with some semi-parametric
estimation method (like least squares) that still produces results
even if you don't have a fully specified likelihood. (It might not be
efficient compared to the case when you have full information, but
your results are less wrong than if your full specification is wrong.)

Mommy! I found a statistically significant penny. I'm rich. :)

> -- Phil
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