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This makes the stopping potential absolutely absurd since no matter how many persons perform the tests on the same data, the results should be consistent. I) are not probability distributions therefore they do not provide the most probable value for a parameter and the most probable values.

These three reasons are enough to get you going into thinking about the drawbacks of the . From here, we’ll first understand the basics of Bayesian Statistics.

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In fact, today this topic is being taught in great depths in some of the world’s leading universities.

With this idea, I’ve created this beginner’s guide on Bayesian Statistics.

Then, the experiment is theoretically repeated infinite number of times but practically done with a stopping intention.

For example, I perform an experiment with a stopping intention in mind that I will stop the experiment when it is repeated 1000 times or I see minimum 300 heads in a coin toss. Now, we’ll understand using an example of coin toss.

I’ve tried to explain the concepts in a simplistic manner with examples.

Prior knowledge of basic probability & statistics is desirable.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.The objective is to estimate the fairness of the coin.Below is a table representing the frequency of heads: We know that probability of getting a head on tossing a fair coin is 0.5. An important thing is to note that, though the difference between the actual number of heads and expected number of heads( 50% of number of tosses) increases as the number of tosses are increased, the proportion of number of heads to total number of tosses approaches 0.5 (for a fair coin).But measured against a sample (fixed size) statistic with some stopping intention changes with change in intention and sample size.

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