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Probability Distributions categories are Continuous Distributions, Probability Distributions categories are Discrete Distributions, Continuous Distributions can be described by Probability Density Functions, Continuous Distributions can be described by Cumulative Density Functions, Exponential Distribution is a special case of the Weibull Distribution, Binomial Distribution is a special case of the Multinomial Distribution, Normal Distribution is implied from the Central Limit Theorem, Discrete Distributions can be described by Cumulative Distribution Function, Discrete Distributions can be described by Probability Mass Functions, Exponential Distribution is a special case of the Gamma Distribution, Chi-Square Distribution is a special case of the Gamma Distribution, Geometric Distribution is a special case of the Negative Binomial Distribution, Probability Distributions can be Multimodal, Probability Distributions can be Bimodal, Probability Distributions can be Symmetric, Probability Distributions can be Unimodal, Probability Distributions can be Positively Skewed, Probability Distributions can be Negatively Skewed, Continuous Distributions can be the Gamma Distribution, Continuous Distributions can be the Rayleigh Distribution, Continuous Distributions can be the Weibull Distribution, Continuous Distributions can be the Normal Distribution, Continuous Distributions can be the Uniform Distribution, Continuous Distributions can be the Chi-Square Distribution, Continuous Distributions can be the Beta Distribution, Continuous Distributions can be the Lognormal Distribution, Continuous Distributions can be the Exponential Distribution, Exponential Distribution sequences lead to Poisson Process, Bernoulli Distribution is a special case of the Binomial Distribution, Discrete Distributions can be the Multinomial Distribution, Discrete Distributions can be the Binomial Distribution, Discrete Distributions can be the Bernoulli Distribution, Discrete Distributions can be the Poisson Distribution, Discrete Distributions can be the Geometric Distribution, Discrete Distributions can be the Negative Binomial Distribution, Gamma Distribution can be used to represent the sum of i.i.d. random variables having an Exponential Distribution, Uniform Distribution is a special case of the Beta Distribution, Lognormal Distribution applies to random variables, the logarithm of which have a Normal Distribution