The correct answer is: A. Bayesian probability
Bayesian probability is a way of thinking about probability that is based on the idea that beliefs are updated in light of new evidence. It is named after Thomas Bayes, who first developed the theory in the 18th century.
Bayesian probability is used in a wide variety of fields, including statistics, machine learning, and decision-making. It is also used in everyday life, when we make decisions based on our beliefs about the world.
For example, let’s say you believe that the probability of rain tomorrow is 0.6. If you then see a weather forecast that says there is a 0.8 chance of rain, you will update your belief to be 0.8. This is because the new evidence (the weather forecast) has changed your prior belief (the probability of rain without the weather forecast).
Bayesian probability is a powerful tool for reasoning about uncertainty. It can be used to make decisions in the face of incomplete information, and to update beliefs in light of new evidence.
Here is a brief explanation of each option:
- Frequency probability is the probability of an event occurring based on the number of times it has occurred in the past. For example, the probability of flipping a coin and getting heads is 0.5, because heads has occurred 50% of the time in the past.
- Frequency inference is the process of making inferences about the probability of an event occurring based on the number of times it has occurred in the past. For example, if you flip a coin 10 times and get heads 7 times, you might infer that the probability of getting heads is 0.7.
- Bayesian inference is the process of updating beliefs about the probability of an event occurring based on new evidence. For example, if you believe that the probability of rain tomorrow is 0.6, and then you see a weather forecast that says there is a 0.8 chance of rain, you will update your belief to be 0.8.