Linked Questions

0 votes
1 answer

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 ...
kte80's user avatar
  • 42
7 votes
0 answers

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 ...
tdc's user avatar
  • 7,559
102 votes
31 answers

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 ...
49 votes
14 answers

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 ...
Elliott's user avatar
  • 543
53 votes
9 answers

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 ...
BYS2's user avatar
  • 1,475
37 votes
8 answers

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.
user333's user avatar
  • 7,101
16 votes
5 answers

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 ...
nibot's user avatar
  • 261
17 votes
2 answers

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 (...
Sie Tw's user avatar
  • 439
11 votes
5 answers

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, ...
Indie's user avatar
  • 113
14 votes
2 answers

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 ...
BCLC's user avatar
  • 2,394
10 votes
3 answers

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 ...
Bi-Gnomial's user avatar
9 votes
3 answers

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 ...
Chill2Macht's user avatar
  • 6,119
20 votes
2 answers

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 ...
mlai's user avatar
  • 493
2 votes
3 answers

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 ...
Bg1850's user avatar
  • 71
3 votes
3 answers

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 : ...
poshtad's user avatar
  • 149
13 votes
1 answer

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 ...
Uwat's user avatar
  • 567
18 votes
2 answers

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. ...
Patrick's user avatar
  • 832
6 votes
2 answers

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 ...
lukstei's user avatar
  • 205
9 votes
1 answer

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 ...
Blade's user avatar
  • 625
5 votes
2 answers

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 ...
Patrick's user avatar
  • 832
13 votes
1 answer

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 ...
Yakkanomica's user avatar
4 votes
5 answers

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 ...
4 votes
2 answers

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 ...
The Pointer's user avatar
  • 1,436
7 votes
2 answers

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 ...
Yurii's user avatar
  • 1,884
1 vote
2 answers

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 ...
pythOnometrist's user avatar
0 votes
1 answer

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 ...
WalterB's user avatar
  • 53
3 votes
2 answers

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 ...
John M's user avatar
  • 2,097
4 votes
2 answers

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 ...
krishnab's user avatar
  • 1,502
1 vote
1 answer

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 ...
Ghilas BELHADJ's user avatar
1 vote
2 answers

(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: ...
Fabio's user avatar
  • 153

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