'Scoring rule' diaporamas de présentation

P roduction and Evaluation in social media

P roduction and Evaluation in social media. Social media: Knowledge Sharing. Online encyclopedia Online question-answer forum CrowdSourcing. Questions. User contributed content E.g. How does the coverage and content of wikipedia grow?

631 views • 50 slides

An Experimental Test of Combinatorial Information Markets

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Scoring Matrices

Scoring Matrices. Diff. Scoring Rules Lead to Diff. Alignments. Example Score = 5 x (# matches) + (-4) x (# mismatches) + + (-7) x (total length of all gaps) Example Score = 5 x (# matches) + (-4) x (# mismatches) + + (-5) x (# gap openings) + (-2) x (total length of all gaps).

1.11k views • 47 slides

Scoring Rules, Generalized Entropy, and Utility Maximization

Scoring Rules, Generalized Entropy, and Utility Maximization. Victor Jose, Robert Nau, & Robert Winkler Fuqua School of Business Duke University. Presentation for GRID/ENSAM Seminar Paris, May 22, 2007. Overview.

472 views • 47 slides

Scoring Rules, Generalized Entropy, and Utility Maximization

Scoring Rules, Generalized Entropy, and Utility Maximization. Robert Nau Fuqua School of Business Duke University (with Victor Jose and Robert Winkler). Presentation for IEOR Seminar Berkeley, October 29, 2006. Overview.

445 views • 43 slides

Introduction to Sequence Alignment

Introduction to Sequence Alignment. Why Align Sequences?. Find homology within the same species Find clues to gene function Practical issues in experiments Find homology in other species Gather info for an evolutionary model Gene families. The Most Visual Way of Aligning Two Sequences.

1.22k views • 41 slides

Class 3: Estimating Scoring Rules for Sequence Alignment

Class 3: Estimating Scoring Rules for Sequence Alignment. Reminder. Last class we discussed dynamic programming algorithms for global alignment local alignment

485 views • 35 slides

Class 3: Estimating Scoring Rules for Sequence Alignment

Class 3: Estimating Scoring Rules for Sequence Alignment. Reminder. Last class we discussed dynamic programming algorithms for global alignment local alignment

463 views • 35 slides

403 views • 33 slides

Probability and Asset Updating Using Bayesian Networks for Combinatorial Prediction Markets

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Assessment of Probability Distributions

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Assessment of Probability Distributions

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The Distortion of Cardinal Preferences in Voting

The Distortion of Cardinal Preferences in Voting. Ariel D. Procaccia and Jeffrey S. Rosenschein. Lecture outline. Distortion. Misrepresentation. Conclusions. Introduction. Introduction to Voting Distortion Definition and intuition Discouraging results Misrepresentation

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Efficient Voting via the T op-k Elicitation Scheme: A Probabilistic Approach

Efficient Voting via the T op-k Elicitation Scheme: A Probabilistic Approach. Joel Oren, University of Toronto Joint work with Yuval Filmus , Institute of Advanced Study. Motivation. Common theme : communication-efficient group decision-making.

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Dominating Manipulations in Voting with Partial Information

Dominating Manipulations in Voting with Partial Information. Paper by: Vincent Conitzer , Toby Walsh and Lirong Xia Presented by: John Postl James Thompson. Motivation.

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Ties Matter: Complexity of Voting Manipulation Revisited

Ties Matter: Complexity of Voting Manipulation Revisited . Edith Elkind (Nanyang Technological University, Singapore ). b ased on joint work with Svetlana Obraztsova (NTU/PDMI) and Noam Hazon (CMU). Synopsis. We will talk about voting and, in particular, voting manipulation

235 views • 16 slides

Fair scores for ensemble forecasts Chris Ferro University of Exeter

Fair scores for ensemble forecasts Chris Ferro University of Exeter. 13th EMS Annual Meeting and 11th ECAM ( 10 September 2013 , Reading, UK). Evaluating ensemble forecasts. Multiple predictions, e.g. model simulations from several initial conditions.

278 views • 12 slides

Automated Design of Voting Rules by Learning From Examples

Automated Design of Voting Rules by Learning From Examples. Ariel D. Procaccia, Aviv Zohar, Jeffrey S. Rosenschein. Outline. Scoring rules. Learning. Limitations. Conclusions. Scoring rules: Definition Advantages Our approach Learning voting rules in the PAC model Main theorem

219 views • 11 slides

Automated Design of Voting Rules by Learning From Examples

Automated Design of Voting Rules by Learning From Examples. Ariel D. Procaccia, Aviv Zohar, Jeffrey S. Rosenschein. Outline. Scoring rules. Learning. Limitations. Conclusions. Scoring rules: Definition Advantages Our approach Learning voting rules in the PAC model Main theorem

231 views • 11 slides