What are prior distributions in Bayesian statistics?

What are prior distributions in Bayesian statistics?

Prior distributions represent our beliefs about a parameter before observing any data. They are crucial in Bayesian analysis.

How do I choose a prior distribution?

Choosing a prior depends on your prior knowledge and the context of the problem. Common choices include uniform, normal, or beta distributions.

Can you give an example of a prior distribution?

Sure! A common example is the beta distribution, often used as a prior for probabilities, especially in binomial models.

What happens if I choose a poor prior?

A poor prior can lead to biased results or misleading conclusions. It's essential to choose a prior that reflects your knowledge accurately.

Are there methods to assess the impact of my prior?

Yes, sensitivity analysis can help assess how different priors affect your posterior results, ensuring robustness in your conclusions.