Cumulative Distribution Function (eBook)

1: Cumulative Distribution Function - Introduces the CDF and its foundational role in probability.

2: Cauchy Distribution - Examines this key probability distribution and its applications.

3: Expected Value - Discusses the concept of expected outcomes in statistical processes.

4: Random Variable - Explores the role of random variables in probabilistic models.

5: Independence (Probability Theory) - Analyzes independent events and their significance.

6: Central Limit Theorem - Details this fundamental theorem's impact on data approximation.

7: Probability Density Function - Outlines the PDF and its link to continuous distributions.

8: Convergence of Random Variables - Explains convergence types and their importance in robotics.

9: MomentGenerating Function - Covers functions that summarize distribution characteristics.

10: ProbabilityGenerating Function - Introduces generating functions in probability.

11: Conditional Expectation - Examines expected values given certain known conditions.

12: Joint Probability Distribution - Describes the probability of multiple random events.

13: Lévy Distribution - Investigates this distribution and its relevance in robotics.

14: Renewal Theory - Explores theory critical to modeling repetitive events in robotics.

15: Dynkin System - Discusses this system's role in probability structure.

16: Empirical Distribution Function - Looks at estimating distribution based on data.

17: Characteristic Function - Analyzes functions that capture distribution properties.

18: PiSystem - Reviews pisystems for constructing probability measures.

19: Probability Integral Transform - Introduces the transformation of random variables.

20: Proofs of Convergence of Random Variables - Provides proofs essential to robotics reliability.

21: Convolution of Probability Distributions - Explores combining distributions in robotics.