PPT-Quantifying Uncertainty in Ecosystem Studies (QUEST)

John L Campbell 1 Ruth D Yanai 2 Mark B Green 13 Carrie Rose Levine 2 Mary Beth Adams 1 Douglas A Burns 4 Donald C Buso 5 Mark E Harmon 6 Trevor Keenan. Lab 2 - . Equations. Tomorrow - Tue 3-5 or 7-9 PM - SN 4117. Assignment 2 – Data Equations. Due Wednesday. Data = Model + Residual. Chapter 5. Data Equations. Data = Model + Residual. Data = Model + Residual. Have They Changed Everything?. Pete Binfield. Co-Founder and Publisher. PeerJ. UBC Open - 10/22/2013. @. ThePeerJ. https://peerj.com. @. p_binfield. pete@peerj.com. http://en.wikipedia.org/wiki/File:Megamouth_shark_japan.jpg. HIV/AIDS knowledge and sexual behavior of female young adults in the Philippines. Michael R.M. . Abrigo. University of Hawai`i at . Manoa. Kris A. Francisco. National Graduate Institute for Policy Studies. Winter . 2010. AIMA3e Chapter 13:. Quantifying Uncertainty. OUTLINE. overview. 1. rationale for a new representational language. what logical representations can't do. 2. utilities & decision theory. for S2D forecasting. EUPORIAS wp31. Nov 2012, Ronald Hutjes. Background. S2D impact prediction. Uncertainty explosion / Skill implosion ??. SST. Weather. (Downscaling). Soil moisture. Plant productivity. Heng. . Ji. jih@rpi.edu. 03/29. , 04/01. ,. . 2016. Top 1 Proposal Presentation. Multimedia . Joint . Model: . Spencer Whitehead . 4.83. ". good idea building on existing system". "interesting problem, clear schedule". Heisenberg. Causality law has it that if we know the present, then we can predict the future.. Be aware: in this formulation, it is not the consequence, but the premise, that is false. As a matter of principle, we cannot know all determining elements of the present.. May 22, 2013. 2.  . Evaluating Uncertainty . Feedback from stakeholders and the Independent Scientific Panel (ISP) stressed that the understanding and evaluation of uncertainty is important in the prioritization. Objective. The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. . We conduct a variance-based sensitivity analysis . of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.. Mountain or Molehill?. Michelle C. Odden, PhD. Outlines. Definitions and DAGs. Quantifying the bias. Should we be worried?. Discussion. Definitions & DAGs. Confounding Bias. Exposure. Outcome. Confounder. U . = Understanding. E . = Extended Thinking Adventure. S. = Summary. T . = Tell. quest CHECK- INVERTS. Directions-. Number your journal #. 1-10.. You will answer questions about . MOLLUSKS.. You . Andrew Levan. For fans of probability, confidence intervals and margins of error, climate change is a dream come true. For everyone else, the fact that uncertainty (inherent in any complex area of science) has gradually become one of climate change's defining features is a constant headache. Because uncertainty – real or manufactured – is a well-rehearsed reason for inaction. Campaigns for FMD. Bradbury, N.V., . Probert. , W.J.M., Shea, K., . Runge. , M.C., . Fonnesbeck. , C.J., Keeling, M.J., Ferrari, M.J. & . Tildesley, M.J.. *. Value of information (VOI) analysis. 1. ERiMA. : . Envisioning Risk Models for Assessment of AI-based applications.. 2. Dr Huma Samin. 1. Post Doctoral Research Associate Computer Science. Durham University, UK. huma.samin@durham.ac.uk.