Beginners often find Bayesian and frequentist statistics confusing due to their fundamentally different approaches to probability and inference. Frequentist statistics, the more traditional method, interprets probability as the long-run frequency of events. This approach relies on repeated sampling and focuse...
We have alsoincluded some up-to-date financial episodes to enliven, for the beginners,the stratified atmosphere of "strictly business". We are indebted to RuthWilliams, who read a draft of the new chapters with valuable suggestionsfor improvement; to Bernard Bru and Marc Barbut for information ...
By taking advantage of the PMF and CDF libraries, it is possible for beginners to learn the concepts and solve challenging problems. This book is under the Creative Commons Attribution-NonCommercial 3.0 Unported License, which means that you are free to copy, distribute, and modify it, as ...
Uses simulation as an accessible way to explain probability and stochastic model behavior to beginners. Covers material from basic probability through to hierarchical Bayesian models and spatial/ spatio-temporal statistical inference. Provides detailed instructions for using R, along with complete R ...
The course was very interesting and offered a broad range of different topics. The concepts were introduced in a simple manner in the videos, and then developed further in the pdf documents, which is nice (especially for beginners, who would benefit from exam...
The authors use clear and concise language throughout the book, making it accessible to beginners, while also providing in-depth coverage of the principles and techniques involved. The exercises and solutions provided also make it an excellent resource for anyone who wants to test their knowledge ...
If they sound scary right now – just hold on for a few minutes. I have explained each concept with an example.I have explained each concept in a simplistic manner to avoid overload of mathematical concepts. At the end of the guide, I have given you two fun and exciting challenges. Go...
Basic Concepts of Prob. (cont.) Conditional Probability :P(B|A)=P(AB)/P(A) P(AB) = P(A)P(B|A) =P(B)P(A|B) So, P(A|B)=P(B|A)P(A)/P(B) (Bayes’ Rule) For independent events, P(AB) = P(A)P(B), so P(A|B)=P(A) Total probability: If A1,...
statistics for big data 21. introduction to time series analaysis 22. time series analysis – i (beginners) 23. time series analysis – ii (intermediate ) 24. time series forecasting part 1: statistical models 25. time series forecasting part 2: arima modeling and tests 26. time series ...