Probability Distribution & Numerical Analysis

In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. Examples are found in experiments whose sample space is non-numerical, where the distribution would be a categorical distribution; experiments whose sample space is encoded by discrete random variables, where the distribution can be specified by a probability mass function; and experiments with sample spaces encoded by continuous random variables, where the distribution can be specified by a probability density function. More complex experiments, such as those involving stochastic processes defined in continuous time, may demand the use of more general probability measures.

Chapter-1 :Discrete Univariate Distributions

Chapter-2 :Continuous Univariate Distributions

Chapter-3 :Exact Sampling Distributions

Chapter-4 :Finite Differences

Chapter-5 :Interpolation

Chapter-6 :Numerical Integration

Chapter-7 :Numerical Differentiation