Bayesian Statistics explained to Beginners in Simple English


Bayesian Statistics continues to remain incomprehensible in the ignited minds of many analysts. Being amazed by the incredible power of machine learning, a lot of us have become unfaithful to statistics. Our focus has narrowed down to exploring machine learning. We fail to understand that machine learning is only one way to solve real world problems. In several situations, it does not help us solve business problems, even though there is data involved in these problems. To say the least, knowledge of statistics will allow you to work on complex analytical problems, irrespective of the size of data. In 1770s, Thomas Bayes introduced'Bayes Theorem'.

Bayesian Learning for Machine Learning: Part 1 - Introduction to Bayesian Learning - DZone AI


In this article, I will provide a basic introduction to Bayesian learning and explore topics such as frequentist statistics, the drawbacks of the frequentist method, Bayes's theorem (introduced with an example), and the differences between the frequentist and Bayesian methods using the coin flip experiment as the example. To begin, let's try to answer this question: what is the frequentist method? When we flip a coin, there are two possible outcomes -- heads or tails. Of course, there is a third rare possibility where the coin balances on its edge without falling onto either side, which we assume is not a possible outcome of the coin flip for our discussion. We conduct a series of coin flips and record our observations i.e. the number of the heads (or tails) observed for a certain number of coin flips. In this experiment, we are trying to determine the fairness of the coin, using the number of heads (or tails) that we observe.

How I Learned to Stop Worrying and Love Uncertainty


Since their early days, humans have had an important, often antagonistic relationship with uncertainty; we try to kill it everywhere we find it. Without an explanation for many natural phenomena, humans invented gods to explain them, and without certainty of the future, they consulted oracles. It was precisely the oracle's role to reduce uncertainty for their fellow humans, predicting their future and giving counsel according to their gods' will, and even though their accuracy left much to be desired, they were believed, for any measure of certainty is better than none. As society grew sophisticated, oracles were (not completely) displaced by empiric thought, which proved much more successful at prediction and counsel. Empiricism itself evolved into the collection of techniques we call the scientific method, which has proven to be much more effective at reducing uncertainty, and is modern society's most trustworthy way of producing predictions.

Bayesian Statistics for Data Science – Towards Data Science


Frequentist Statistics tests whether an event (hypothesis) occurs or not. It calculates the probability of an event in the long run of the experiment. A very common flaw found in frequentist approach i.e. dependence of the result of an experiment on the number of times the experiment is repeated. Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. It provides people the tools to update their beliefs in the evidence of new data.

Frequentists Fight Back


Frequentist-leaning statisticians have numerous responses to Bayesian criticisms that may not be widely known. Broadly speaking, these rebuttals assert that Bayesian criticisms of Frequentist approaches rely on circular arguments, are self-refuting, rest mostly on semantics, or are mainly of interest to academics and irrelevant in practice. Below, I've briefly summarized the ones I'm aware of from memory and in my own words. The meaning of the term is often unclear. Is it objective Bayes, subjective Bayes, approximate Bayes, empirical Bayes, or all of the above?