# Bayesian Inference

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Alright, folks, let’s talk Bayesian Inference, the statistical superhero of prediction. Think of it as Batman, but instead of a utility belt, it has a complex formula that calculates the probability of an event. So, let’s suit up and use this statistical caped crusader to make some sharper predictions from our data!

## What is Bayesian Inference?

Bayesian inference is a statistical approach that uses Bayes’ theorem to update our prior beliefs or knowledge about a hypothesis or event based on new evidence or data. The approach is based on the idea that we can update our opinions based on the likelihood of observing the evidence given the hypothesis or event.

Bayesian inference differs from frequentist inference, which only considers the likelihood of observing the data given the null hypothesis. Bayesian inference, on the other hand, considers both the prior probability of the hypothesis and the possibility of observing the data given the hypothesis.

## Why do we use Bayesian Inference?

We use Bayesian inference because it allows us to incorporate prior knowledge or beliefs into our statistical analysis. This can be particularly useful in situations where we have some prior knowledge or experience with a problem.

Bayesian inference also provides a way to quantify uncertainty in our predictions and estimates. This can be useful in decision-making, where we must understand the uncertainty associated with our predictions.

## How does Bayesian Inference work?

Bayesian inference works by updating our prior beliefs about a hypothesis or event based on new evidence or data. Bayes’ theorem provides a way to do this by calculating the posterior probability of the hypothesis given the data.

The posterior probability is calculated as the product of the prior probability and the likelihood of observing the data given the hypothesis, divided by the probability of observing the data, summed over all possible hypotheses:

## How to use Bayesian Inference to get sharper predictions from your data?

To use Bayesian inference to obtain sharper predictions from your data, you can follow these steps:

1. Define your prior beliefs or knowledge about the hypothesis or event of interest.
2. Collect data that is relevant to the hypothesis or event.
3. Define a likelihood function that describes the probability of observing the data given the hypothesis.
4. Use Bayes’ theorem to calculate the posterior probability of the hypothesis given the data.
5. Use the posterior probability to make predictions or estimates.
6. Assess the uncertainty of your predictions or estimates by calculating credible intervals or posterior distributions.

In conclusion, Bayesian inference is a statistical approach that allows us to update our prior beliefs or knowledge about a hypothesis or event based on new evidence or data. It is useful when we have prior knowledge or experience with a problem and provides a way to quantify uncertainty in our predictions and estimates. Following the steps outlined above, we can use Bayesian inference to obtain sharper predictions from our data.

## Meet Ryan

(Your Analytics and CRO Super 🤓)

What most people find incredibly complex (enter: GA4 and sequential testing analysis) Ryan thoroughly enjoys (and is damm good at). 