What Prior probability is
Prior probability is the probability of an event occurring before any new data is collected or before taking into account any new information. This probability is based on existing data and is also known as the prior probability distribution.
Steps for Prior Probability:
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Identify the population of interest: The first step in calculating prior probability is to identify the population of interest. This could be any group of people or objects that you want to study.
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Collect existing data: The next step is to collect existing data on the population of interest. This data can include demographic information, past behavior, or any other relevant information.
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Calculate the probability of an event: Once you have collected the existing data, you can then calculate the probability of an event occurring in the population of interest.
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Use the prior probability to make predictions: Once you have calculated the prior probability of an event, you can then use it to make predictions about the likelihood of the event occurring in the future.
Examples
- Bayesian inference: Using prior probability to update beliefs about the probability of an event after observing new evidence.
- Naive Bayes classification: Making predictions about the probability of an event based on prior beliefs.
- Maximum a posteriori estimation: Estimating a model parameter using prior probability as a basis.
- Prior predictive distribution: Using prior probability to estimate the likelihood of future outcomes.