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- 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)
- mathematical statistics - 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 vs frequentist Interpretations of Probability
Bayesian probability frames problems in e g statistics in quite a different way, which the other answers discuss The Bayesian system seems to be a direct application of the theory of probability, which seeks to avoid inferring anything which is not already known, and only inferring based on exactly what has been observed
- bayesian - Flat, conjugate, and hyper- priors. What are they? - Cross . . .
Today, Gelman argues against the automatic choice of non-informative priors, saying in Bayesian Data Analysis that the description "non-informative" reflects his attitude towards the prior, rather than any "special" mathematical features of the prior (Moreover, there was a question in the early literature of at what scale a prior is
- Posterior Predictive Distributions in Bayesian Statistics - Physics Forums
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
- bayesian - Can someone explain the concept of exchangeability . . .
The concept is invoked in all sorts of places, and it is especially useful in Bayesian contexts because in those settings we have a prior distribution (our knowledge of the distribution of urns on the table) and we have a likelihood running around (a model which loosely represents the sampling procedure from a given, fixed, urn)
- What is the best introductory Bayesian statistics textbook?
My bayesian-guru professor from Carnegie Mellon agrees with me on this having the minimum knowledge of statistics and R and Bugs(as the easy way to DO something with Bayesian stat) Doing Bayesian Data Analysis: A Tutorial with R and BUGS is an amazing start You can compare all offered books easily by their book cover!
- Should Bayesian inference be avoided with a small sample size and . . .
With small n and no reliable prior, instead of a Bayesian analysis---or even a Frequentist analysis (which may just confirm that "The sample is too small to estimate these parameters with adequate precision")---I would just report descriptive statistics graphs and be very transparent about the study's limitations: due to the sample size, our
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