Bayesian vs. Frequentist Inference

Are you Bayesian or Frequentist?

Regarding the probability school of thought, there are two main approaches: the Bayesian and the frequentist approaches. Both of these approaches have their own strengths and weaknesses, and they are often used in different circumstances to help determine the probability of events.

So, what are their differences? Is one better than the other? This is a wrong question to ask; Bayesian thinking is based on the idea that probabilities represent a degree of belief in an event occurring. This approach is often used when there is uncertainty about the likelihood of an event occurring or when new information becomes available that can affect the probability of an event occurring.

On the other hand, the frequentist approach is based on the idea that the probability of an event occurring is equal to the long-run frequency with which that event occurs. This approach is often used when the probability of an event is determined by collecting data and observing how often the event occurs in a large number of trials.

To understand the differences better, let’s go through a simple example. Imagine a box with a bunch of marbles in it, and you want to know what color marbles are in the box. In the Bayesian approach, you might start by guessing that there are mostly red marbles in the box because you have seen many red marbles in similar containers. As you reach into the box and pull out marbles, you can update your guess based on the colors you see. So, if you pull out a bunch of blue marbles, you might start to think that there are primarily blue marbles in the box, and you can adjust your guess accordingly.

In the frequentist approach, you would not make a guess about the colors of the marbles in the box. Instead, you would just reach in and start pulling out marbles, counting the number of red, blue, and other colored marbles you see. After you have pulled out a large number of marbles, you can calculate the probability…

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