PPT-Probabilistic Data Management

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. However subjects make discrete responses and report the phenomenal contents of their mind to be allornone states rather than graded probabilities How can these 2 positions be reconciled Selective attention tasks such as those used to study crowding However the exact compu tation of association probabilities jk in JPDA is NPhard where jk is the probability that th observation is from th track Hence we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless N 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”. (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. 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?. 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. We have not addressed the question of why does this classifier performs well, given that the assumptions are unlikely to be satisfied.. The linear form of the classifiers provides some hints.. . 1. . – What Next?. Martin Theobald. University of Antwerp. Joint work with . Maximilian Dylla, Sairam Gurajada, Angelika . Kimmig. , Andre . Melo. , Iris Miliaraki, . Luc de . Raedt. , Mauro . Sozio. (November 16, PLWG). Contents. Introduction. Deterministic Vs Probabilistic. Applications . Efforts and Issues. Next Steps & Summary. Appendices. 2. Introduction. Motivation. Increasing uncertainties (intermittent generation, . 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 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 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query. Hybrid . Recommender Systems. Pigi Kouki, . Shobeir. . Fakhraei. , . James . Foulds. , . Magdalini. . Eirinaki. , . Lise. . Getoor. University . of California, Santa Cruz . University of Maryland, College .