Linked Questions
34 questions linked to/from Bayesian and frequentist reasoning in plain English
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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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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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31
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Is there a way to remember the definitions of Type I and Type II Errors?
I'm not a statistician by education, I'm a software engineer. Yet statistics comes up a lot. In fact, questions specifically about Type I and Type II error are coming up a lot in the course of my ...
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Clarification on interpreting confidence intervals?
My current understanding of the notion "confidence interval with confidence level $1 - \alpha$" is that if we tried to calculate the confidence interval many times (each time with a fresh sample), it ...
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Bayesian vs frequentist Interpretations of Probability
Can someone give a good rundown of the differences between the Bayesian and the frequentist approach to probability?
From what I understand:
The frequentists view is that the data is a repeatable ...
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What is Bayes' theorem all about?
What are the main ideas, that is, concepts related to Bayes' theorem?
I am not asking for any derivations of complex mathematical notation.
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Is there more to probability than Bayesianism?
As a student in physics, I have experienced the "Why I am a Bayesian" lecture perhaps half a dozen times. It is always the same -- the presenter smugly explains how the Bayesian interpretation is ...
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Relation between MAP, EM, and MLE
I am a beginner in machine learning. I can do programming fine but the theory confuses me a lot of the times.
What is the relation between Maximum Likelihood Estimation (MLE), Maximum A posteriori (...
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Why would perfectly similar data have 0 mutual information?
I'm not a statistic major, so my knowledge of statistics is quite limited but I've found myself in need of learning about and using mutual information. I believe I understand the concept and formula, ...
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Bayesian logit model - intuitive explanation?
I must confess that I previously haven't heard of that term in any of my classes, undergrad or grad.
What does it mean for a logistic regression to be Bayesian? I'm looking for an explanation with a ...
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Bayesian AB testing
I am running an AB Test on a page that receives only 5k visits per month. It would take too long to reach traffic levels necessary to measure a +-1% difference between the test and control. I have ...
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When (and why) do Bayesians reject valid Bayesian methods? [closed]
From what I have read and from answers to other questions I have asked here, many so-called frequentist methods correspond mathematically (I don't care if they correspond philosophically, I only care ...
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How to apply Bayes' theorem to the search for a fisherman lost at sea
The article The Odds, Continually Updated mentions the story of a Long Island fisherman who literally owes his life to Bayesian Statistics. Here's the short version:
There are two fishermen on a ...
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3
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Predicting future values with a regression model
I have a set of predictor variables and a target variable. I am really confused with regards to what method to use for forecasting the target variable.
For example, my data set has monthly customer ...
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If I only have a range, is it acceptable to calculate an average out of it?
Supposed I have only this data point available:
Concentration = (1.1 - 2.0 g/L).
Is it acceptable to conclude :
...
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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 ...
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Frequentism and priors
Robby McKilliam says in a comment to this post:
It should be pointed out that, from the frequentists point of view, there is no reason that you can't incorporate the prior knowledge into the model. ...
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Bayesian vs. frequentist estimation
I don't really understand the connection between bayesian to "normal" frequentist estimation.
Suppose we want to estimate the expected value of a population given a sample.
In frequentist statisics ...
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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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Bayesian and frequentist optimization and intervals
I realize the methodology pursued by the Frequentist and Bayesian camps generally differ. However, one method of estimation that they do share is optimization of a certain function:
Frequentists ...
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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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Which expansions and identities are useful to applied statisticians? [closed]
Simple mathematical relationships like $V(X) = E(X^2) - E(X)^2$, aside from being theoretical results, are useful because they allow analysts to do back-of-the-envelope calculations, restate results ...
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Why would considering $\theta$ to be a random variable not be 'Bayesian'?
I am currently studying the textbook In All Likelihood -- Statistical Modelling and Inference Using Likelihood by Yudi Pawitan. Section Inverse probability: the Bayesians of chapter 1 says the ...
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Estimation derived from ignorance
Is something wrong with the following reasoning? Mostly I wonder how could one derive uniformly random arrival from ignorance. But even if that derivation is invalid generally, it seems reasonable ...
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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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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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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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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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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 ...
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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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