Continuous uniform distribution

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Explores the Carpan collector problem, analyzing expected completion times and waiting times for collecting different objects uniformly at random.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Explores ceramic forming methods, emphasizing particle packing, compaction, additives, ejection, and slip casting practical aspects.

Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.

Covers phase space, ensembles of particles, probability distributions, and probability density functions.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Explains the EM algorithm for estimating lite-bulbs lifetime and discusses its application to heavy-bulbs.

Covers the estimation of R through balanced dice, uniform, hypergeometric, and Poisson distributions.

Explains moments, expectation, variance, and quantiles in probability distributions with examples like Poisson and exponential distributions.