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- What exactly is a Bayesian model? - Cross Validated
A Bayesian model is a statistical model made of the pair prior x likelihood = posterior x marginal Bayes' theorem is somewhat secondary to the concept of a prior
- When are Bayesian methods preferable to Frequentist?
People do use Bayesian techniques for regression But because the frequentist methods are very convenient and many people are pragmatic about which approach they use, so often people who are happy to use either will use ordinary regression if there's no need for something more complicated But as soon as you need to deal with a bit more complexity, or to formally incorporate prior information
- Who Are The Bayesians? - Cross Validated
What distinguish Bayesian statistics is the use of Bayesian models :) Here is my spin on what a Bayesian model is: A Bayesian model is a statistical model where you use probability to represent all uncertainty within the model, both the uncertainty regarding the output but also the uncertainty regarding the input (aka parameters) to the model
- bayesian - Flat, conjugate, and hyper- priors. What are they? - Cross . . .
Flat priors have a long history in Bayesian analysis, stretching back to Bayes and Laplace A "vague" prior is highly diffuse though not necessarily flat, and it expresses that a large range of values are plausible, rather than concentrating the probability mass around specific range
- Posterior Predictive Distributions in Bayesian Statistics
Confessions of a moderate Bayesian, part 4 Bayesian statistics by and for non-statisticians Read part 1: How to Get Started with Bayesian Statistics Read part 2: Frequentist Probability vs Bayesian Probability Read part 3: How Bayesian Inference Works in the Context of Science Predictive distributions A predictive distribution is a distribution that we expect for future observations In other
- bayesian - What is the difference between R hat and psrf . . . - Cross . . .
In convergence diagnosis in WinBUGS JAGS Stan, there are different statistics reported for each variable In WinBUGS Stan, Rhat ($\\hat{R}$) is reported In JAGS with the runjags package, psrf (Pote
- Should Bayesian inference be avoided with a small sample size and . . .
To the contrary, objective Bayesian priors have the effect of smoothing parameter estimates in small samples and can be helpful The classical example of this phenomenon is the reference Beta (0 5,0 5) prior with the binomial likelihood
- bayesian - What exactly does the term inverse probability mean . . .
We could use a Bayesian posterior probability, but still the problem is more general than just applying the Bayesian method Wrap up Inverse probability might relate to Bayesian (posterior) probability, and some might view it in a wider sense (including fiducial "probability" or confidence intervals)
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