Reverend Thomas Bayes derived a theorem for probability inference that is widely used by statisticians today. Known as Bayes' Theorem or Bayes' Rule, it suggests that probability assessments should be updated based on new information received. Bayesian statistics applies this theorem to predict the likelihood of events by using prior distributions and observed data to calculate posterior distributions. Strategic Analysis Enterprises provides decision support solutions using quantitative tools like applied statistical models and Bayesian statistics under the direction of CEO Steve Shellman.

1. A Brief Overview of
Bayes Theorem and
Bayesian Statistics

2. A Brief Overview of Bayes Theorem
and Bayesian Statistics
Steve Shellman is the founder, CEO, and chief scientist at Strategic Analysis
Enterprises, Inc. Under the direction of Stephen Shellman, the company provides
decision support solutions to national organizations using quantitative and
qualitative analysis tools, among them applied statistical models and Bayesian
statistics.

3. A Brief Overview of Bayes Theorem
and Bayesian Statistics
Reverend Thomas Bayes was a British mathematician who derived a theorem for
probability inference that is widely used by statisticians today in several
industries, including government and science. As circumstances change
constantly and information is dynamic, the Bayes Theorem or Bayes Rule suggests
that the information used to make a probability assessment should not remain
static. As such, the rule is based on a model where an assumption must be
continually updated based on new information received. The original assumption
is referred to as the prior distribution, and the new data used to revise the prior
is known as observed distribution. The value obtained through a calculation using
both prior and observed distribution is known as posterior distribution.

4. A Brief Overview of Bayes Theorem
and Bayesian Statistics
Bayesian statistics applies this theorem to predict the likelihood of something
happening, such as an event or a type of social behavior. Application of Bayesian
inference is used for binomial proportion, Poisson mean, normal mean, mean
difference, normal standard deviation, and the Simple Linear Regression Model.