Learn Priors and Modeling Assumptions | Foundations of Bayesian Thinking

Prior distributions are a central concept in Bayesian statistics, representing your beliefs about unknown parameters before seeing any data. In mathematical terms, if you are interested in a parameter $θ$ , the prior distribution is written as $P(θ)$ . This distribution encodes your assumptions or knowledge about $θ$ before observing any evidence. The choice of prior directly reflects what you believe is plausible or likely for $θ$ , and these beliefs can be based on previous studies, expert knowledge, or sometimes a lack of information.

The mathematical form of a prior can take many shapes, such as a normal distribution, beta distribution, or uniform distribution, depending on the parameter and the context. For example, if you are modeling the probability of success in a binomial experiment, you might use a beta distribution as your prior for the probability parameter. The parameters of the prior distribution (such as mean and variance) are chosen to reflect your assumptions: a prior centered around 0.5 might indicate you believe both outcomes are equally likely, while a prior centered near 0 or 1 would indicate a strong belief in one outcome.

Priors play a crucial role in Bayesian modeling because they combine with the likelihood (the probability of observing the data given the parameter) to produce the posterior distribution, which is the updated belief about the parameter after seeing the data. This process is expressed by Bayes' theorem:
$P(θ | data) ∝ P(data | θ) × P(θ)$ where $P(θ | data)$ is the posterior, $P(data | θ)$ is the likelihood, and $P(θ)$ is the prior. The prior acts as a starting point for inference, and its influence depends on both its form and the amount of data you have. With little data, the prior can have a strong effect on the posterior; with a large amount of data, the likelihood tends to dominate, and the influence of the prior diminishes.

Choosing a prior is not just a technical step — it is a modeling decision that encodes your assumptions about the world. These assumptions can be explicit, such as using a prior based on previous experiments, or implicit, such as choosing a uniform prior to express ignorance. Understanding how priors reflect and influence modeling assumptions is essential for responsible Bayesian analysis.

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Priors can be classified as informative or non-informative. Informative priors incorporate substantial knowledge or strong beliefs about a parameter, often based on previous research or expert opinion. Non-informative (or weakly informative) priors are chosen to have minimal influence, reflecting ignorance or neutrality about parameter values. Exploring the differences between these types of priors and their uses can deepen your understanding of Bayesian modeling.

How do different priors affect the posterior?

The choice of prior can significantly influence the posterior, especially with limited data. A strong informative prior may dominate the inference, while a non-informative prior allows the data to have more influence. As more data is collected, the likelihood's impact increases, and the prior's effect decreases.

What happens if the prior contradicts the data?

If the prior strongly disagrees with the observed data, the resulting posterior may represent a compromise between the prior and the likelihood. This can lead to slower updating of beliefs or, in extreme cases, result in posteriors that seem at odds with the data, especially when the prior is very strong or the data set is small.

When should you use an informative vs non-informative prior?

Use an informative prior when you have reliable prior knowledge or expert opinion about the parameter. Use a non-informative prior when you want the data to drive inference, or when you lack strong prior beliefs. The choice should be justified based on the context of the problem and the goals of your analysis.