Lectures related to Pearson distribution

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Covers the coordination of physics assistantship for electromagnetism course.

Covers the frequency factor approach and the log-Pearson Type III distribution for estimating maximum streamflow.

Explores the Jarzynski Equality through experimental tests on RNA molecules, validating the theory in non-equilibrium conditions.

Explains the structure of the master's in mechanical engineering program, including master project and internship options.

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Explores important distribution functions like Bernoulli, Binomial, and Uniform in probability theory.

Covers software optimizations to improve program performance by maximizing cache hits and optimizing work distribution.

Explores testing and estimating continuous distributions through interval partitioning and expected number calculations.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

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

Covers software optimization, cache efficiency, parallel scheduling, and work distribution strategies for fast parallel programs.

Covers the theory of sampling, focusing on statistics for mathematicians.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores various continuous probability distributions and expectation of random variables.

Covers practical points, charge densities, Coulomb's law, and nuclear fission examples.

Explores distribution of primes, arithmetic progressions, Mersene primes, and Goldbach's Conjecture.

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

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