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
  • 7,579
107 votes
33 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 ...
50 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
  • 553
56 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
  • 1,505
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
  • 7,261
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
  • 2,444
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
  • 6,369
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
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
  • 852
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
  • 587
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
  • 655
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
  • 852
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
  • 2,096
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
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
  • 1,974
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
  • 2,147
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
  • 1,522
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
1 vote
0 answers

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.
user143951's user avatar
2 votes
0 answers

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...
Sally7874's user avatar
1 vote
0 answers

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 ...
chatGPT's user avatar
  • 127
0 votes
1 answer

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 ...
hehe's user avatar
  • 211