PPT-Probabilistic Data Management

Chapter 3 Probabilistic Query Answering 1 2 Objectives In this chapter you will Learn the challenge of probabilistic query answering on uncertain data Become familiar with the framework for probabilistic . Component-Based Shape Synthesis. Evangelos. . Kalogerakis. , . Siddhartha . Chaudhuri. , . Daphne . Koller. , . Vladlen. . Koltun. Stanford . University. Goal: generative model of shape. Goal: generative model of shape. David Kauchak. CS451 – Fall 2013. Admin. Assignment 6. Assignment . 7. CS Lunch on Thursday. Midterm. Midterm. mean: 37. median: 38. Probabilistic Modeling. training data. probabilistic model. train. Bhargav Kanagal & Amol Deshpande. University of Maryland. Introduction. Correlated Probabilistic data generated in many scenarios. Data Integration [AFM06]: Conflicting information best captured using “mutual exclusivity”. in human semantic memory. Mark . Steyvers. , Tomas L. Griffiths, and Simon Dennis. 소프트컴퓨팅연구실. 오근현. TRENDS in Cognitive Sciences vol. . 10, . no. . 7, 2006. Overview . Relational models of memory. Madhu Sudan. . MIT CSAIL. 09/23/2009. 1. Probabilistic Checking of Proofs. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. Can Proofs Be Checked Efficiently?. How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. University of Washington. Machine Learning. Traditional Programming. Machine Learning. Computer. Data. Algorithm. Arijit Khan. Systems Group. ETH Zurich. Lei Chen. Hong . Kong University of Science and Technology. Social Network. Transportation Network. Chemical Compound. Biological Network. Graphs are Everywhere. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. T. he . cost of computing an exact representation of the . configuration . space of a . free-flying 3D object, or a multi-joint . articulated object . is . often . prohibitive. But . very fast algorithms exist that can check if . Chapter 2: . Data . Uncertainty Model. 2. Objectives. In this chapter, you will:. Learn the formal definition of uncertain data. Explore different granularities of data uncertainty. Become familiar with different representations of uncertain data. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . 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).