15,194 research outputs found
Cosmological parameter inference with Bayesian statistics
Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found
their place in the field of Cosmology. They have become important mathematical
and numerical tools, especially in parameter estimation and model comparison.
In this paper, we review some fundamental concepts to understand Bayesian
statistics and then introduce MCMC algorithms and samplers that allow us to
perform the parameter inference procedure. We also introduce a general
description of the standard cosmological model, known as the CDM
model, along with several alternatives, and current datasets coming from
astrophysical and cosmological observations. Finally, with the tools acquired,
we use an MCMC algorithm implemented in python to test several cosmological
models and find out the combination of parameters that best describes the
Universe.Comment: 30 pages, 17 figures, 5 tables; accepted for publication in Universe;
references adde
On Universality in Human Correspondence Activity
Identifying and modeling patterns of human activity has important
ramifications in applications ranging from predicting disease spread to
optimizing resource allocation. Because of its relevance and availability,
written correspondence provides a powerful proxy for studying human activity.
One school of thought is that human correspondence is driven by responses to
received correspondence, a view that requires distinct response mechanism to
explain e-mail and letter correspondence observations. Here, we demonstrate
that, like e-mail correspondence, the letter correspondence patterns of 16
writers, performers, politicians, and scientists are well-described by the
circadian cycle, task repetition and changing communication needs. We confirm
the universality of these mechanisms by properly rescaling letter and e-mail
correspondence statistics to reveal their underlying similarity.Comment: 17 pages, 3 figures, 1 tabl
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