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www.panix.com | ||
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www.randomservices.org
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| | | | | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight. | |
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sriku.org
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| | | | | [AI summary] The article explains how to generate random numbers that follow a specific probability distribution using a uniform random number generator, focusing on methods involving inverse transform sampling and handling both continuous and discrete cases. | |
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statsandr.com
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| | | | | This article explains in details what is the normal or Gaussian distribution, its importance in statistics and how to test if your data is normally distributed | |
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almostsuremath.com
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| | | A stochastic process X is said to have independent increments if $latex {X_t-X_s}&fg=000000$ is independent of $latex {\{X_u\}_{u\le s}}&fg=000000$ for all $latex {s\le t}&fg=000000$. For example, standard Brownian motion is a continuous process with independent increments. Brownian motion also has stationary increments, meaning that the distribution of $latex {X_{t+s}-X_t}&fg=000000$ does not depend on t. In... | ||
