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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
- 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
- When are Bayesian methods preferable to Frequentist?
The Bayesian, on the other hand, think that we start with some assumption about the parameters (even if unknowingly) and use the data to refine our opinion about those parameters Both are trying to develop a model which can explain the observations and make predictions; the difference is in the assumptions (both actual and philosophical)
- bayesian - Can somebody explain to me NUTS in english? - Cross Validated
You're incorrect that HMC is not a Markov Chain method Per Wikipedia: In mathematics and physics, the hybrid Monte Carlo algorithm, also known as Hamiltonian Monte Carlo, is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult This sequence can be used to approximate the distribution (i e , to
- What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please
- bayesian - What are posterior predictive checks and what makes them . . .
I understand what the posterior predictive distribution is, and I have been reading about posterior predictive checks, although it isn't clear to me what it does yet What exactly is the posterior
- 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
- 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
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