Prior refers to the initial value or probability distribution assigned to a parameter before any evidence or data is taken into account, commonly used in Bayesian econometrics and statistics.
Updated: Jun 17, 2024

3 key takeaways

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  • A prior represents the initial belief about a parameter before incorporating new data.
  • Priors are essential in Bayesian inference, influencing the posterior distribution.
  • Choosing an appropriate prior can significantly affect the outcomes of Bayesian analysis.

What is a prior?

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In the context of Bayesian statistics and econometrics, a prior (or prior distribution) is the initial probability distribution assigned to a parameter based on existing knowledge or assumptions before any new data is observed.

The prior reflects the initial beliefs about the parameter’s possible values and serves as the starting point for Bayesian inference.

Importance of priors in Bayesian inference

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Priors are fundamental to Bayesian inference, which combines prior information with new data to update beliefs about a parameter. The process involves:

  1. Prior Distribution: The initial distribution represents the beliefs about the parameter.
  2. Likelihood: The probability of observing the data given the parameter.
  3. Posterior Distribution: The updated distribution of the parameter after combining the prior and the likelihood, reflecting both the prior beliefs and the new evidence.

The formula for updating the prior to obtain the posterior distribution is given by Bayes’ theorem:

Posterior ∝ Likelihood × Prior

Types of priors

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There are different types of priors, each serving various purposes depending on the context and available information:

  1. Informative Priors: Priors that incorporate specific, substantive information or expert knowledge about the parameter. These are used when substantial prior information is available.
  2. Non-informative (or Weakly Informative) Priors: Priors that contain minimal information, designed to have little influence on the posterior distribution. These are used when there is little to no prior knowledge about the parameter.
  3. Conjugate Priors: Priors that, when combined with the likelihood, result in a posterior distribution of the same family as the prior. These simplify the computational process of Bayesian inference.
  4. Empirical Priors: Priors derived from the data itself or from related datasets, providing a data-driven approach to specifying priors.

Example of a prior in Bayesian inference

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Consider estimating the probability of success (θ) in a Bernoulli trial (e.g., flipping a coin). Suppose we start with a prior belief that the coin is fair, assigning a uniform prior distribution to θ, meaning every value of θ between 0 and 1 is equally likely:

Prior: θ ~ Uniform(0, 1)

After observing data (e.g., 10 heads in 20 flips), we update our belief using the likelihood of observing the data given θ. The posterior distribution combines the prior and the likelihood to provide an updated belief about θ.

Choosing an appropriate prior

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Selecting a suitable prior is crucial in Bayesian analysis because it can influence the posterior distribution, especially when the data is limited. The choice of prior should be based on:

  • Prior Knowledge: Using existing knowledge or expert opinions about the parameter.
  • Context: Considering the context and the goals of the analysis.
  • Sensitivity Analysis: Assessing how different priors affect the posterior distribution to ensure robust conclusions.

Priors play a vital role in Bayesian econometrics and statistics, shaping the initial assumptions about parameters before new data is incorporated. By understanding and carefully choosing priors, analysts can enhance the accuracy and reliability of their Bayesian inferences.

For further insights, explore related topics such as Bayesian inference, posterior distribution, and likelihood functions.

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