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 . 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 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. 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. 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. 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. Pérez. Nicolás. . Suárez. CRIDA A.I.E.. COmbining. Probable . TRAjectories. — COPTRA. Brussels 5. th. of October . 2016. COmbining. Probable . TRAjectories. — COPTRA. 2. Introduction. COPTRA . 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 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 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. 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. Hybrid . Recommender Systems. Pigi Kouki, . Shobeir. . Fakhraei. , . James . Foulds. , . Magdalini. . Eirinaki. , . Lise. . Getoor. University . of California, Santa Cruz . University of Maryland, College . 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).