Jacob’s Highlights I recently attended the Workshop on Animal Population Imaging (APPNING 2018) held after the ISMRM conference in Paris. It was a well run workshop highlighting the benefits and troubles with large scale animal population imaging (https://appning2018.sciencesconf.org/). Several Highlights from the workshop: Quantitative Connectomic Histology Presented by GA Johnson from Duke University Used Diffusion Tensor Imaging to assess how the brain was connected in the mouse. Attempt to determine the structural connectivity of mouse brain and compare with the tracer studies performed at the Allen Brain Institute Tractograms currently have more invalid than valid bundles, and therefore they are attempting to be as accurate as possible Goal was to push the technology as hard as possible to get the best resolution and images possible First scan that pushed the boundaries was a 120 direction DTI scan with b-values up to 4000 s/mm2.

Introduction library(tidyverse) library(matlib) library(knitr) library(RColorBrewer) The purpose of this document is to understand the parameter and residuals error estimates in a basic linear regression model when working with binary categorical variables. Recall the general model definition: \[ \mathbf{y} = \mathbf{X}\mathbf{\beta} + \mathbf{e}\] where \(\mathbf{X}\) is the design matrix and \(\mathbf{\beta}\) is a \((p+1)\)-vector of coefficients/parameters, including the intercept parameter. The errors are normally distributed around 0 with variance \(\sigma^2\): \[e \sim N(0,\sigma^2) \quad .

I was recently extremely fortunate to attend ICLR 2018, albeit as something of an interloper. Accordingly, what follows is surely a rather atypical highlight reel. All pedantry and any inaccuracy is, of course, due to my own limited understanding of these elegant topics and the breadth of their application. Causal reasoning and graphical models There is a well-developed modern theory of causal inference and reasoning based on graphical models developed by Judea Pearl and others.