PPT-System Aspects of Probabilistic Data Management

Magdalena Balazinska Christopher Ré and Dan Suciu University of Washington One slide overview of motivation Data are uncertain in many applications Business Dedup Info Extraction. March 19-21, 2012. Did you vote in the last Presidential election?. A. Yes B. No. . A B C D E. Medico-Legal Aspects of Healthcare: Emerging Policy Issues. March 19-21, 2012. Pawan Kumar Gupta. Lecturer . Psychiatry. Systemic Lecture MBBS 6. th. semester . dated: 31. st. august 2014. introduction. How psychiatric and medical illness are inter-related. Why to study psychiatric aspects of medical illness. a . new framework for student . reflection. Rachel Lofthouse & Roger Knill,. School of Education, Communication & Language . Sciences. ,. Newcastle . University. TEAN Conference . 21. st. M. of . Modern Cryptography. Josh Benaloh. Brian . LaMacchia. Winter 2011. Agenda. Integrity Checking (HMAC . redux. ). Protocols (Part 1 – Session-based protocols). Introduction. Kerberos. SSL/TLS. of . Modern Cryptography. Josh Benaloh. Brian . LaMacchia. Winter 2011. Some Tools We’ve Developed. Homomorphic Encryption. Secret . Sharing. Verifiable Secret Sharing. Threshold Encryption. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. By Kendall and Mitchell. What an introvert is:. An introvert is someone who loses energy in social interactions, and feels re-energized while in solitude. Their social circles are condensed. They’re content with having a fewer amount of friends. . Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Josh Benaloh. Tolga Acar. Fall 2016. October 25, 2016. 2. The wiretap channel. Key (K. 1. ). Key (K. 2. ). Eavesdropper. Plaintext. (P). Noisy insecure. channel. Encrypt. Decrypt. Alice. Bob. Plaintext. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. Assessing risk, . considering chances and uncertainties.. What is Probability?. “A strong likelihood or chance of something” (dictionary.com). “The likelihood of something occurring or the chance of something happening” (yourdictionary.com). CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access). Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .