# Bayesian updating dating from ukraine

The focus is on the pattern of biases in information processing.How do we go about calculating a posterior with a prior N~(a, b) after observing n data points? Imagine that you want to estimate mean $\mu$ of normal distribution and $\sigma^2$ is known to you. We assume normal prior for $\mu$ with hyperparameters $\mu_0,\sigma_0^2:$ \begin X\mid\mu &\sim \mathrm(\mu,\ \sigma^2) \ \mu &\sim \mathrm(\mu_0,\ \sigma_0^2) \end Since normal distribution is a conjugate prior for $\mu$ of normal distribution, we have closed-form solution to update the prior \begin E(\mu' \mid x) &= \frac \[7pt] \mathrm(\mu' \mid x) &= \frac \end Unfortunately, such simple closed-form solutions are not available for more sophisticated problems and you have to rely on optimization algorithms (for point estimates using paper by Kevin P. Check also Do Bayesian priors become irrelevant with large sample size?

Salt could lose its savour." Indeed, there are non-Bayesian updating rules that also avoid Dutch books (as discussed in the literature on "probability kinematics") following the publication of Richard C.

Jeffrey's rule, which applies Bayes' rule to the case where the evidence itself is assigned a probability.

Here is a ten-minute overview of the fundamental idea. But there's a catch: Sometimes the arithmetic can be nasty.

On your way to the hotel you discover that the National Basketball Player's Association is having a convention in town and the official hotel is the one where you are to stay, and furthermore, they have reserved all the rooms but yours.

(Statistics) statistics the fundamental result which expresses the conditional probability P(E/A) of an event E given an event A as P(A/E).