Ratio of counts vs ratio of rates
in Poisson processes
Giulio D'Agostini
Università “La Sapienza” and INFN, Roma, Italia
(giulio.dagostini@roma1.infn.it,
http://www.roma1.infn.it/~dagos)
arXiv:2012.04455 [stat.ME]
Abstract:
The often debated issue of `ratios of small numbers of events'
is approached from a probabilistic perspective,
making a clear distinction between the predictive problem
(forecasting numbers of events we might count under
well stated assumptions, and therefore of their ratios)
and inferential problem (learning about the
relevant parameters of the related probability distribution,
in the light of the observed number of events).
The quantities of interests and their relations are
visualized in a graphical model (`Bayesian network'),
very useful to understand how to approach the problem
following the rules of probability theory.
In this paper, written with didactic intent, we
discuss in detail the basic ideas, however giving
some hints of how real life complications,
like (uncertain) efficiencies and possible
background and systematics, can be included in the analysis,
as well as the possibility that the ratio of rates might depend
on some physical quantity.
The simple models considered in this paper allow to obtain,
under reasonable assumptions, closed expressions for the
rates and their ratios.
Monte Carlo methods are also used, both to cross check the exact results
and to evaluate by sampling the ratios of counts in the cases
in which large number approximation does not hold.
In particular it is shown how to make approximate inferences
using a Markov Chain Monte Carlo using JAGS/rjags.
Some examples of R and JAGS code are provided.