PPT-MEGN 537 – Probabilistic Biomechanics

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. (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. Ch.3 – 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. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. 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 .