# Bayesian inference
An approach to perception from Bayes' Theorem.
In the note What is the difference in between perception, inference, judgement?, I have an example of:
- Perception: Walked in with a wet umbrella
- Inference: It must be raining outside
This example can be explained from Bayesian inference like this:
- Prior: My belief of the weather in the UK:
- Rain (frequent)
- Snow/sleet (infrequent)
- Likelihood: Chance of these causing wet umbrella
- Rain (likely)
- Snow/sleet (likely)
- Conclusion:
- It's raining outside (likely + frequent)
# References
Goldstein, Cognitive Psychology (p. 68).
Bayesian inference was named after Thomas Bayes (1701-1761), who proposed that our estimate of the probability of an outcome is determined by two factors: (1) the prior probability or simply the prior, which is our initial. Belief about the probability of an outcome and (2) the extent to which the available evidence is consistent with the outcome.