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Mathematic homework exercise in LaTeX

The very basic LaTeX example/template file that the Journal of Computational and Graphical Statistics provides (as of 2015-09-02)

Math 453 HW 1 (with solutions)

Hello World in LaTeX

This is the title page for University of Amsterdam's MoL thesis. For more information, please see https://www.illc.uva.nl/MScLogic/current-students/graduation/titlepage.html.

With Overleaf, edit online instantly this IEEE MTT-S Conference Paper (IMS 2014), and download a PDF version. This project is also available on my web site

Version 2.0.1 por Msc. Ing. Sergio Ramón Toledo Gallardo del 10 de junio de 2015 La compilación debe realizarse utilizando el comando pdflatex o pdflatexmk (para Mac) Los programas necesarios son Miktex 2.9 (distribución latex) y algún procesador como el texmaks o texniccenter Para texnicCenter Usar makeindex y habilitar usar bibtex Al imprimir el PDF se debe seleccionar tamaño real y Elegir origen por tamaño de pagina PDF para respetar los margenes requeridos Las figuras deben cargarse en la carpeta "imagenes" en formato pdf para optimizar la velocidad de compilación, sin embargo también se permiten formatos jpg y eps. Las referencia bibliográficas se cargan en el archivo fiuna.bib

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.

La generación de numeros aleatorios por computadora es de vital importancia para muchas ramas del conocimiento, como por ejemplo, la simulacion de gases, fluidos y por supuesto en la criptografia. Esto ha ocasionado el desarrollo de varias tecnicas para generarlos. A continuacion analizamos algunos algoritmos que consideramos son muy buenos por varios motivos.