What exactly is a Bayesian model? - Cross Validated
Dec 14, 2014 · 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 …
What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please.
Choosing Bayesian Priors - Cross Validated
Jan 7, 2024 · With noninformative priors, however, you deprive yourself of the main advantages of Bayesian modelling. For instance, carefully chosen informative priors expand the space of …
Difference between Bayesian networks and Markov process?
Mar 17, 2016 · What is the difference between a Bayesian Network and a Markov process? I believed I understood the principles of both, but now when I need to compare the two I feel …
Structural Equation Models (SEMs) versus Bayesian Networks (BNs)
Structural equation models and Bayesian networks appear so intimately connected that it could be easy to forget the differences. The structural equation model is an algebraic object.
How to choose prior in Bayesian parameter estimation
Dec 15, 2014 · The problem is that if you choose non-conjugate priors, you cannot make exact Bayesian inference (simply put, you cannot derive a close-form posterior). Rather, you need to …
How do you apply constrains on parameters in Bayesian modeling?
Mar 11, 2020 · In Bayesian setting we are dealing with posterior distribution, that is defined in terms of likelihood and priors $$ p (\theta | X) \propto p (X | \theta) \, p (\theta) $$ If you need …
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 $\theta$ can a probability …
Is probabilistic modeling the same thing as Bayesian modeling?
"Is Bayesian modeling within probabilistic modeling?" - yes. Frequentist methods for instance are probabilistic methods which are not Bayesian. Bayesian approaches look at posterior …
bayesian - What's a good prior distribution for degrees of …
I want to use a t distribution to model short interval asset returns in a bayesian model. I'd like to estimate both the degrees of freedom (along with other parameters in my model) for the distribu...