Linked Questions
34 questions linked to/from Bayesian and frequentist reasoning in plain English
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Can someone explain why Bayesian networks are called "Bayesian"
I have been reading Jensen's book on Bayesian Networks and Decision Graphs as well as the Deep Learning book by Bengio, et. al. I am trying to understand why undirected graphs are referred to as ...
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Question about the true nature of errors
In frequentist statistics, in regression analysis, errors, like random variables, have a distribution. Errors, like parameters, can be estimated and the residuals of the model are their estimates. So ...
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Bayesian : Comparing means of two posterior samples/ Help a Frequentist Out
UPDATE Thanks for the many thoughtful responses and questions! I've made edits here to clarify further. and also respond to each respondent individually.
Original Post
I have two sets of posterior ...
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(Failure) probability calculation
I am working on mortality in 12 hospitals performing cardiac surgery in babies. The dataset is available here: Surg dataset. The dataset is structured in this way:
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When can't frequentist sampling distribution be interpreted as Bayesian posterior in regression settings?
My actual questions are in the last two paragraphs, but to motivate them:
If I am attempting to estimate the mean of a random variable that follows a Normal distribution with a known variance, I've ...
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Estimating probability of success, given a reference population
Suppose you have the following situation:
You observed over time 1000 bowling players, who each played a relatively small number of games (say 1 to 20). You noted the strike percentage for each of ...
- 567
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1
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Bayesian Bootstrap interpretation
I am using Bayesian Bootstrap for some analysis. Given dataset $X=\{x_1, \dots, x_N\}$, we generate bootstrapped samples $X_1,\dots, X_K$ by sampling from the $X$, with replacement. In classical ...
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Include prior knowledge in regression model
I've a classical dataset with real attributes and I want to perform a regression.
But, not all the entries in the training dataset are trustworthy; there is an attribute that I can turn into a ...
- 210
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Repeated Measures ANOVA post hoc test (bayesian)
I am trying to understand the procedure of carrying out a Bayesian Repeated Measures ANOVA. In a conventional repeated measures ANOVA, I calculate the effect of a certain parameter (e.g., study ...
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1
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Bayesian vs. frequentist view [duplicate]
I have tried to figure out the difference between the two views of looking at the world: Bayesian and frequentist.
Can someone please let me know if I have it right? (Please do not refer me to some ...
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1
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What to do with important features?
I am currently solving the titanic problem in kaggle. The data of the problem consists of several features such as "sex", "class in society", etc., and you are to predict whether a person survived the ...
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Probabilistic (Bayesian) vs Optimisation (Frequentist) methods in Machine Learning [duplicate]
Possible Duplicate:
Bayesian and frequentist reasoning in plain English
A very similar question was posed on stats.SE: Bayesian and frequentist reasoning in plain English, which provoked some ...
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203
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What is Bayesian and Monte Carlo Simulation? [duplicate]
Can someone explain in plain language for a layperson what are Bayesian and Monte Carlo simulations and the relationship between the two? I thought Bayesian was the same as Monte Carlo Simulation...
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Does bayesians' critique to frequentists apply to themselves too?
I've been reading about bayesians versus frequentists, including articles in this forum (like this one). Key is of course the issue of "priors". The bayesian critique being that frequentists ...
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The difference between the Frequentist, Bayesian and Fisherian appraoches to statistical inference [duplicate]
I'm just trying to get my head around the differences between these three approaches to statistical inference. I'm just not entirely sure what the significant differences are between the three.
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