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MLE of a Bernoulli Distribution and a Binomial Distribution

MLE of a Continuous Uniform Distribution

MLE of a Normal Distribution and a Mixture of Normal Distributions

Derivative of a Determinant with respect to a Matrix

Derivative of a Quadratic Form with respect to a Vector

Derivative of a Trace with respect to a Matrix

Maximum Likelihood Estimates for a Multivariate Normal Distribution

The Score Function - Asymptotic Normality

Kaplan Meier Estimator as an MLE

MLE for a Wishart Distribution (central)

MLE of a Gumbel Distribution (part 1)

MLE for a Gumbel Distribution (part 2)

MLEs of a Gamma Distribution (part 1)

MLEs of a Gamma Distribution (part 2)

MLE of a Negative Binomial Distribution

MLEs for a Beta Distribution (part 1)

Method of Moments and MLEs for a Beta Distribution (part 2)

MLE of a Multinomial Distribution

MLEs of a Double Exponential Distribution

Using R to Generate Double Exponential Data and Calculate the MLEs

MLEs of an Inverse Gamma Distribution

Using R to find the MLEs and Method of Moments estimators for an Inverse Gamma Distribution

Generating Data and deriving the MLE for a 2 Parameter Lindley Distribution

Maximum Likelihood Estimators Beta Binomial Distribution

Using R: Method of Moments and ML estimators for Beta Binomial Distribution

Description:

Dive deep into the world of Maximum Likelihood Estimation (MLE) with this comprehensive 3.5-hour tutorial. Explore a wide range of probability distributions, including Bernoulli, Binomial, Continuous Uniform, Normal, Multivariate Normal, Wishart, Gumbel, Gamma, Negative Binomial, Beta, Multinomial, Double Exponential, Inverse Gamma, Lindley, and Beta Binomial. Learn essential mathematical techniques such as matrix derivatives, quadratic forms, and trace operations. Discover the Score Function and its asymptotic normality, as well as the Kaplan Meier Estimator. Gain practical experience using R for data generation, MLE calculations, and method of moments estimations. Master the theoretical foundations and practical applications of MLE across various statistical distributions, enhancing your skills in statistical inference and data analysis.

Maximum Likelihood Estimation

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#Mathematics #Statistics & Probability #Data Science #Data Analysis #Programming #Domain-Specific Languages (DSL) #R Programming #Parameter Estimation #Maximum Likelihood Estimation

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