13,272 research outputs found
Bayesian astrostatistics: a backward look to the future
This perspective chapter briefly surveys: (1) past growth in the use of
Bayesian methods in astrophysics; (2) current misconceptions about both
frequentist and Bayesian statistical inference that hinder wider adoption of
Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian
modeling as a major future direction for research in Bayesian astrostatistics,
exemplified in part by presentations at the first ISI invited session on
astrostatistics, commemorated in this volume. It closes with an intentionally
provocative recommendation for astronomical survey data reporting, motivated by
the multilevel Bayesian perspective on modeling cosmic populations: that
astronomers cease producing catalogs of estimated fluxes and other source
properties from surveys. Instead, summaries of likelihood functions (or
marginal likelihood functions) for source properties should be reported (not
posterior probability density functions), including nontrivial summaries (not
simply upper limits) for candidate objects that do not pass traditional
detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in
"Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed.,
Springer, New York, forthcoming in 2012), the inaugural volume for the
Springer Series in Astrostatistics. Version 2 has minor clarifications and an
additional referenc
Bayesian Hierarchical Modelling for Tailoring Metric Thresholds
Software is highly contextual. While there are cross-cutting `global'
lessons, individual software projects exhibit many `local' properties. This
data heterogeneity makes drawing local conclusions from global data dangerous.
A key research challenge is to construct locally accurate prediction models
that are informed by global characteristics and data volumes. Previous work has
tackled this problem using clustering and transfer learning approaches, which
identify locally similar characteristics. This paper applies a simpler approach
known as Bayesian hierarchical modeling. We show that hierarchical modeling
supports cross-project comparisons, while preserving local context. To
demonstrate the approach, we conduct a conceptual replication of an existing
study on setting software metrics thresholds. Our emerging results show our
hierarchical model reduces model prediction error compared to a global approach
by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on
Mining Software Repositories May 28--29, 2018, Gothenburg, Swede
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
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