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REWRITING UBER ENGINEERING: THE OPPORTUNITIES MICROSERVICES PROVIDE
A case of study at Uber, how they wrote a micro service for currency exchange that scales across their multiple data centers, from methodology to implementation to testing to actual deployment. Some of the technology used includes: MVCS, UDR, Python, Tornado, TChannel, Thrift, Hailstorm, uContainer, uDestroy
microservices  uber  architecture  scalability  articles  reference  implementation  methodologies  deploy  persistence  python  testing  load  containers  soa 
september 2016 by oscar-lopez
[1603.05334] A general convex framework for multiple testing with prior information
Using prior information may improve power in frequentist multiple testing. P-value weighting is a promising methodology where each test is conducted at a different level, using critical values based on independent prior data. However, existing methods are limited, and do not allow the user to specify properties of the weights that are desired in practice, such as boundedness, or monotonicity in the strength of prior evidence.
Here we develop a general framework for p-value weighting based on convex optimization. This allows flexible constraints and leads to a variety of new methods, such as bounded and monotone weights, stratified weights, and smooth weights. It also recovers several existing heuristics. Finally we focus on the promising special case of bounded monotone weights. These are appealing as they increase with the strength of prior evidence, and stable because they are within pre-specified bounds. We show they have good empirical power in the analysis of genome-wide association studies, better than any weighting method we have seen.
statistics  multiple-testing  methodologies  rather-interesting  nudge-targets 
march 2016 by Vaguery
[1403.4630] Penalising model component complexity: A principled, practical approach to constructing priors
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys' priors, are designed to support Occam's razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.
they're-never-going-to-learn-about-multiple-objectives-are-they?  statistics  methodologies  system-of-professions  le-sigh 
march 2016 by Vaguery

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