Bayesian inference

Bayesian inference is an approach to statistics whereby all forms of uncertainty are described in terms of probability.

Bayesian inference applies Bayes' theorem to observations in order to infer the probability of the truth of an hypothesis.

This is an iterative process which constantly updates the probability of the truth of the hypothesis as new data become available.

The alternative is a classical frequentist approach,[1] which takes far more time and money and may not be conclusive. Also, transparency is typically far better for Bayesian inference than for other approaches.

In medical studies, pediatricians are often the specialty most receptive to the statistical approach of Bayesian inference.

A Bayesian analysis applied to remdesivir demonstrated how ineffective that Fauci-promoted medication was for COVID-19.[2]

## References[edit]

1. ↑ https://stats.stackexchange.com/questions/322464/what-is-the-difference-between-classical-frequentist-methods-and-likelihood-meth
2. ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301659/pdf/pone.0255093.pdf