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### Bayesian Statistics Explained in Simple English For Beginners

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 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 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.

### 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.

### Understanding the applications of Probability in Machine Learning

Probability is a measure of uncertainty. Probability applies to machine learning because in the real world, we need to make decisions with incomplete information. Hence, we need a mechanism to quantify uncertainty – which Probability provides us. Using probability, we can model elements of uncertainty such as risk in financial transactions and many other business processes. In contrast, in traditional programming, we work with deterministic problems i.e. the solution is not affected by uncertainty.