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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
- 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 - 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
- Bayesian vs frequentist Interpretations of Probability
The Bayesian interpretation of probability as a measure of belief is unfalsifiable Only if there exists a real-life mechanism by which we can sample values of θ θ can a probability distribution for θ θ be verified In such settings probability statements about θ θ would have a purely frequentist interpretation
- bayesian - How would you explain Markov Chain Monte Carlo (MCMC) to a . . .
The Bayesian landscape When we setup a Bayesian inference problem with N N unknowns, we are implicitly creating a N N dimensional space for the prior distributions to exist in Associated with the space is an additional dimension, which we can describe as the surface, or curve, of the space, that reflects the prior probability of a particular
- Bayesian : Comparing means of two posterior samples Help a Frequentist . . .
Calculating posterior of difference given posterior of two means How should I compare posterior samples of the same parameter from two Bayesian models? Any references would be most welcome Question 1) who do you think is right and why? Question 2) a reference would help a lot
- Bayesian updating with new data - Cross Validated
This is the central computation issue for Bayesian data analysis It really depends on the data and distributions involved For simple cases where everything can be expressed in closed form (e g , with conjugate priors), you can use Bayes's theorem directly The most popular family of techniques for more complex cases is Markov chain Monte Carlo
- What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please
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