PPT-Modelling & Datatypes

John Hughes Software Software Programs Data Modelling Data A big part of designing software is modelling the data in an appropriate way Numbers are not good for this We model the data by defining . N. University of Cambridge* new construction of a finite set of strictness properties for any lazy alge- braic datatype is presented. The construction is based on the categorical view of the solution Modelling for Engineering Processes. Peter Hale UWE. University of the West of England, Bristol. Abstract. Problem. -. Enable translation of human problems/representation to computer models and code.. Name: _______________________________. TG: _________. Class: ____________________. When you see the POP symbol it means you have a chance to get to the next NC level and the teacher will be checking your work for progress.. 3.5. Demonstrate understanding of how technological modelling supports technological development. Aims for this session. To share key messages for technological modelling level 3. T. o develop understanding of how . John Hughes. Software. Software = Programs + Data. Modelling Data. A big part of designing software is . modelling the data. in an appropriate way. Numbers are not good for this!. We model the data by defining . Day 2. :. Session 7. Social Science, Different Purposes and Changing Networks. Discussion: the . Social Science view of ABM. 2-Day Introduction to Agent-Based Modelling, Manchester, Feb/Mar 2013, slide . Lecturer in Quantitative Social Sciences. A basic linear regression model. e. Y. X. Y = B0 B1*X e. What’s the problem?. Assume that the residuals (e) are independent from each other.. Ie. that the model has accounted for everything systematic . Tonight's agenda . Our focus is always somewhere else. A Secure Development Lifecycle?. Threat Modelling. Taking it in your STRIDE. How . to get everyone involved. How to win at Poker. Q & A. Fin. John Hughes. Software. Software = Programs Data. Modelling Data. A big part of designing software is . modelling the data. in an appropriate way. Numbers are not good for this!. We model the data by defining . Dr Linda Bird. 2. nd. – 4. th. December 2012. Meeting Goals. Finalise draft CIMI Laboratory Results Report . mindmaps. Update CIMI Laboratory Results Report . ADL 1.5. Drafts prepared by Tom & Ian. Dr Linda Bird. 26. th. June 2013. Agenda. Background. CIMI . Modelling. Approach. CIMI . Modelling. . Foundations. CIMI . Modelling. Methodology. Future Work. Tomorrow. :. Terminology Binding. background. Barotse. floodplain, Zambia. . Tom Willis. 1. , Mark Smith. 1. , Donall Cross. 2. , Andrew Hardy. 3. , Georgina Ettritch. 3. , Happiness Malawo. 4. , . Mweemba. Sinkombo. 4. , Cosmas Chalo. 4. , Elizabeth Mroz. Issy . Codron. , University of Exeter. Stefan Kraus, Tyler Gardner, . Sorabh. Chhabra, Daniel Mortimer, Owain Snaith, Yi Lu. John Monnier, Antoine . Mérand. , and MIRC-X/MYSTIC Team. Disc Misalignments & Modelling the Inner AU of HD 143006. Prof. Dr. . Steffen Flessa. Department of Health Care Management. University of Greifswald. Population: 82,000,000. Prof. Dr. Steffen Fleßa. 1966. Married, 2 children. BA, MBA, . PhD, .