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Wallplanner for Aug to Oct 2014 latex source generated using https://github.com/perl2ruby/wallplanner/blob/master/wallplanner.rb, output of: ./wallplanner.rb 2014 8..10 requires baposter.cls available here (http://www.brian-amberg.de/uni/poster/baposter/baposter.cls) changed snakes to decorations in baposter.cls

Tesis de Maestria en Electrica.

Beamer - DICIS-UG

Private template for my team work

pgf-pie is a LaTeX package for drawing pie charts (and some interesting variants on pie charts) with the PGF/TikZ graphics package. The examples in this document are from the pgf-pie manual, version 0.2. The source code for pgf-pie is available at http://code.google.com/p/pgf-pie/.

The presentation template for Language Science Press presentations.

In this experiment I looked into a way fo measuring the ground bounce generated by capacitively loading 5 outputs of a Texas Instrument small outline package Hex-Flip Flop chip and discharging them all simultaneously.

Template pour un mémoire ou rapport de projet avec une structure classique : Page de Garde Abstract / Résumé Sommaire Présentation Existant Besoin (Partie supplémentaire) Résultat Bilan Annexe(s) Bibliographie De nombreux exemples de mise en forme et de syntaxe LaTeX. Le template est commenté pour faciliter l'adaptation et la modification du document.

Information before unblinding regarding the success of confirmatory clinical trials is highly uncertain. Estimates of expected future power which purport to use this information for purposes of sample size adjustment after given interim points need to reflect this uncertainty. Estimates of future power at later interim points need to track the evolution of the clinical trial. We employ sequential models to describe this evolution. We show that current techniques using point estimates of auxiliary parameters for estimating expected power: (i) fail to describe the range of likely power obtained after the anticipated data are observed, (ii) fail to adjust to different kinds of thresholds, and (iii) fail to adjust to the changing patient population. Our algorithms address each of these shortcomings. We show that the uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing the resulting posterior distribution to estimate power. We devise MCMC-based algorithms to implement sample size adjustments after the first interim point. Bayesian models are designed to implement these adjustments in settings where both hard and soft thresholds for distinguishing the presence of treatment effects are present. Sequential MCMC-based algorithms are devised to implement accurate sample size adjustments for multiple interim points. We apply these suggested algorithms to a depression trial for purposes of illustration.