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  2. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    The log-normal distribution has also been associated with other names, such as McAlister, Gibrat and Cobb–Douglas. [4] A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive.

  3. Geometric Brownian motion - Wikipedia

    en.wikipedia.org/wiki/Geometric_Brownian_motion

    For the simulation generating the realizations, see below. A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift. [1]

  4. Stochastic process - Wikipedia

    en.wikipedia.org/wiki/Stochastic_process

    The key assumption of the Black-Scholes model is that the price of a financial asset, such as a stock, follows a log-normal distribution, with its continuous returns following a normal distribution. Although the model has limitations, such as the assumption of constant volatility, it remains widely used due to its simplicity and practical ...

  5. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    The distribution of the sum (or average) of the rolled numbers will be well approximated by a normal distribution. Since real-world quantities are often the balanced sum of many unobserved random events, the central limit theorem also provides a partial explanation for the prevalence of the normal probability distribution.

  6. Logarithm - Wikipedia

    en.wikipedia.org/wiki/Logarithm

    When the logarithm of a random variable has a normal distribution, the variable is said to have a log-normal distribution. [75] Log-normal distributions are encountered in many fields, wherever a variable is formed as the product of many independent positive random variables, for example in the study of turbulence. [76]

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  8. Gaussian process - Wikipedia

    en.wikipedia.org/wiki/Gaussian_process

    In practical applications, Gaussian process models are often evaluated on a grid leading to multivariate normal distributions. Using these models for prediction or parameter estimation using maximum likelihood requires evaluating a multivariate Gaussian density, which involves calculating the determinant and the inverse of the covariance matrix.

  9. Prisoners of Profit - The Huffington Post

    projects.huffingtonpost.com/prisoners-of-profit?...

    In 2001, an 18-year-old committed to a Texas boot camp operated by one of Slattery’s previous companies, Correctional Services Corp., came down with pneumonia and pleaded to see a doctor as he struggled to breathe.