PPT-MEGN 537 – Probabilistic Biomechanics

Ch3 Quantifying Uncertainty Anthony J Petrella PhD Bioengineering Big Picture Why study traditional probability Ch 2 Unions and intersections allow us to conceptualize system level variability with multiple sources. (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. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Applying the AMV Method with a. Finite Element Model. Anthony J Petrella, PhD. The Model. Can be analytical or computational. MATLAB for analytical. Finite elements for computational. ABAQUS. ANSYS. SolidWorks Simulation (COSMOS). humerus. and carpals. Skeleton of the Hand. The carpus (wrist) consists of 8 small bones (carpals). Two rows of carpal bones. Proximal row - scaphoid, lunate, triquetrum, pisiform. Distal row - trapezium, trapezoid, capitate, hamate. Prof. Anthony J. Petrella. Bone Material Properties. Bone Macrostructure. Long bone. Epiphysis. Diaphysis. Compact bone (cortical). Spongy bone (cancellous). 1. www.agen.ufl.edu/~chyn/age2062/lect/lect_19/lect_19.htm. 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. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. 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 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. 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 .