It is the most widely used statistical inference technique in the statistical world. Therefore, it is important to understand the difference between the two and how there exists a thin line of demarcation! The debate between frequentist and bayesian have haunted beginners for centuries.
Inherent Flaws in Frequentist Statistics.Understand the methods to test the significance of the model, like p-value, confidence interval, etc.Learn the drawbacks of frequentist statistics and how it leads to the need for Bayesian Statistics.Discover Bayesian Statistics and Bayesian Inference Bayesian Statistics Example.
By the end of this article, you will have a concrete understanding of Bayesian Statistics and its associated concepts. You should check out this course to get a comprehensive low down on statistics and probability. Prior knowledge of basic probability & statistics is desirable. In this beginner’s guide on Bayesian Statistics, I’ve tried to explain the concepts in a simplistic manner with examples. In the 1770s, Thomas Bayes introduced the ‘Bayes Theorem.’ Even centuries later, the importance of ‘Bayesian Statistics’ hasn’t faded away. To say the least, knowledge of statistics will allow you to work on complex data analysis problems in machine learning and data science, irrespective of the size of the data. In several situations, it does help us solve business problems, even when there is data involved in these problems. Bayesian Statistics (bayesian probability) continues to remain one of the most powerful things in the ignited minds of many statisticians.
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