The frequentist tag has no wiki summary.
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Under what conditions do Bayesian and frequentist point estimators coincide?
With a flat prior, the ML (frequentist -- maximum likelihood) and the MAP (Bayesian -- maximum a posteriori) estimators coincide.
More generally, however, I'm talking about point estimators derived ...
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0answers
24 views
What if the MVUE depends on the parameter?
The minimum variance, unbiased estimator $\hat \theta$ of $\theta$ is defined by
$$\hat \theta = \text{argmin}_{\hat \theta} \; \mathbb{E} \left( (\hat \theta - \theta)^2 \, | \, \theta\right), \quad ...
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4answers
364 views
Bayesian uninformative priors vs. frequentist null hypotheses: what's the relationship?
I came across this image in a blog post here.
I was disappointed that reading the statement did not illicit the same facial expression for me as it did for this guy.
So, what is meant by the ...
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12 views
Is it possible to combine bayesian models with frequentist selection?
I am thinking about methods for analysing neuroimaging data. At the moment with a model free frequentists approach (likelihood ratio tests) there is a huge multiple comparison problem - I have seen it ...
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0answers
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Confusion related to Kulldorff's scan statistics
I was reading this paper related to Bayesian spatial scan statistics where I came across the Kulldorff's scan statistics.
I have attached the screenshot of the paper. My objective is to find a ...
2
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1answer
70 views
Priors for parameters of normal distribution leading to same results as frequentist formula
Given a sample vector $x$ of size $N$ from a normally distributed population.
With frequentist methods the population mean is estimated as $\hat{\mu}=\frac{\Sigma{}x_i}{N}$, population sigma is ...
0
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1answer
51 views
Which is the null hypothesis for testing whether I've broken my simulation?
The situation: I'm writing agent-based computer simulations in which there are random effects which can be biased by various parameters. I run the simulation with the same parameters many times in ...
3
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1answer
88 views
On FDA guidance about Bayesian practice
US FDA authorizes the use of Bayesian statistics with informative priors (in certain contexts):
...
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31 views
Anomaly prediction confidence for frequentist vs bayesian parameter inference
I am comparing the behavior of some implementations of Bayesian and frequentist approaches to parametric anomaly detection and currently trying to figure out the differences when the sample set is ...
5
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1answer
158 views
Bayesian and frequentist approaches: What are some success stories for the former? [duplicate]
Possible Duplicate:
Examples of Bayesian and frequentist approach giving different answers
What are some practical examples where a Bayesian approach has an edge over frequentist ...
2
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2answers
159 views
Results Difference: Frequentist vs. Bayesian
I fit a lognormal model on some data points using both frequentist and Bayesian (using a non-informative prior) approaches. However, I got different results. Here are my codes and outputs:
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0answers
37 views
Defining Empirical Risk Minimization
I am reading Machine Learning - A probabilistic Perspective by Kevin Murphy and in chapter 6.5 the author discusses Empirical Risk Minimization, and provides the following definition:
...
2
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2answers
138 views
Questions on paramatric and non-parametric bootstrap
I am reading the chapter on Frequent Statistics from Kevin Murphy's book "Machine Learning - A Probabilistic Perspective". The section on bootstrap reads:
The bootstrap is a simple Monte Carlo ...
1
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1answer
68 views
how do I interpret the following hypothesis test?
Let's say I have two hypotheses for a coin with probability $p$ for heads:
$H_0$ - the null hypothesis - the coin is fair $p = 0.5$.
$H_1$ - coin is unfair $p \neq 0.5$.
Say the test is $|X-n/2| ...
4
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1answer
91 views
how would you approach giving an introductory class about Bayesian statistics?
I need to give a lecture about Bayesian statistics, introducing it to people who have already basic knowledge of classic statistics (but not too much of it in general).
I want to start with some ...
0
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1answer
136 views
Dealing with a small sample size
Suppose we have a data set consists of, say, 5 or 10 observations. The only thing we know about this set is that it came from a positive right skewed distribution. Now suppose we want to fit a ...
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2answers
125 views
Frequentist confidence intervals = constant trapping probability?
In the case of estimating an unknown mean of a normal distribution with known variance, if I'm not mistaken, the confidence interval contains $\theta$ with probability $1 - \alpha$, regardless of the ...
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5answers
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Examples of Bayesian and frequentist approach giving different answers
Note: I am aware of philosophical differences between Bayesian and frequentist statistics.
For example "what is the probability that the coin on the table is heads" doesn't make sense in frequentist ...
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8answers
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What's wrong with XKCD's Frequentists vs. Bayesians comic?
This xkcd comic (Frequentists vs. Bayesians) makes fun of a frequentist statistician who derives an obviously wrong result.
However it seems to me that his reasoning is actually correct in the ...
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5answers
546 views
List of situations where a Bayesian approach is simpler, more practical, or more convenient
There have been many debates within statistics between Bayesians and frequentists. I generally find these rather off-putting (although I think it has died down). On the other hand, I've met several ...
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frequentist coverage, and besides?
One day I gave a $95\%$-confidence interval to a requester who know nothing about statistics. He asked me what does it mean. Roughly, I answered "The population parameter is inside the interval $95\%$ ...
3
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2answers
139 views
Are there any other interpretations besides bayesian and frequentist?
I am aware of the frequentist and bayesian interpretations of statistics. I prefer Bayesian because I think it's closer to how people think, and because we in practice often can't rerun a trial a ...
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0answers
62 views
Random sample of population sub sample size
I am generating models and each model is a random sample of the total model population. It is recommended that I generate 30,000 models and cluster taking the top 5 to 10 clusters to reach the native ...
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1answer
139 views
Frequentist statistics references for someone well versed in modern probability theory
Coming from a rigorous background in analysis and modern probability theory, I find Bayesian statistics straightforward and easy to understand, and frequentist statistics incredibly confusing and ...
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538 views
Do Bayesians ever argue there are cases in which their approach generalizes/overlaps with the frequentist approach?
Do Bayesians ever argue that their approach generalizes the frequentist approach, because one can use non-informative priors and therefore, can recover a typical frequentist model structure?
Can ...
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6answers
652 views
What is the connection between credible regions and Bayesian hypothesis tests?
In frequentist statistics, there is a close connection between confidence intervals and tests. Using inference about $\mu$ in the $\rm N(\mu,\sigma^2)$ distribution as an example, the $1-\alpha$ ...
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530 views
If you use a point estimate that maximizes $P(x | \theta)$, what does that say about your philosophy? (frequentist or Bayesian or something else?)
If somebody said
"That method uses the MLE the point estimate for the parameter which maximizes $\mathrm{P}(x|\theta)$, therefore it is frequentist; and further it is not Bayesian."
would you ...
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2answers
197 views
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 ...
2
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2answers
240 views
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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1answer
185 views
Expectation of an estimator?
When evaluating an estimator in a frequentist setting, using MSE and let say to compute the Bias of the estimator we compute the expectation of this estimator, are we supposing that the estimator has ...
5
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0answers
74 views
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 ...
2
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1answer
275 views
What are the modeling approaches in this cartoon?
What are the modeling approaches depicted here? Can you name them and their prominent proponents or a landmark model? Is there an accepted superior approach? Who prefers which approach?
(From: ...
2
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2answers
675 views
Apriori algorithm in plain English?
I read wiki article about Apriori. I have the trouble in understanding the prune and Join step. Can anyone explain me how Apriori algorithm works in simple terms(such that Novice like me can ...
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2answers
294 views
Are there differences in Bayesian and frequentist approaches to EDA?
Very simply put: Are there any differences in Bayesian and Frequentist approaches to Exploratory Data Analysis?
I know of no inherent biases in EDA methods as a histogram is a histogram, a ...
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5answers
423 views
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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2answers
212 views
Can confidence intervals be added?
For example, say we have three coins. Due to several tests, we have 95% confidence intervals for X (the heads percentage) of each coin. Coin A is 0-5%, coin B is 1-9%, and coin C is 40-70%.
...
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0answers
484 views
Is it OK to do additive smoothing before applying Pearson's chi-square test for independence?
I'm concerned about treating my data as gold, especially in areas of low data support, so I would like to apply additive smoothing. I'm then doing several things with this data, and one of them is ...
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Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research?
While attending conferences, there has been a bit of a push by advocates of Bayesian statistics for assessing the results of experiments. It is vaunted as both more sensitive, appropriate, and ...
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2answers
257 views
Statistical landscape
Has anyone written a brief survey of the various approaches to statistics? To a first approximation you have frequentist and Bayesian statistics. But when you look closer you also have other ...
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4answers
2k views
Confidence intervals for regression parameters: Bayesian vs. classical
Given two arrays x and y, both of length n, I fit a model y = a + b*x and want to calculate a 95% confidence interval for the slope. This is (b - delta, b + delta) where b is found in the usual way ...
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6answers
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What's the difference between a confidence interval and a credible interval?
Joris and Srikant's exchange here got me wondering (again) if my internal explanations for the the difference between confidence intervals and credible intervals were the correct ones. How you would ...
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5answers
1k views
Do working statisticians care about the difference between frequentist and bayesian inference?
As an outsider, it appears that there are two competing views on how one should perform statistical inference.
Are the two different methods both considered valid by working statisticians?
Is ...
17
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5answers
415 views
When can you use data-based criteria to specify a regression model?
I've heard that when many regression model specifications (say, in OLS) are considered as possibilities for a dataset, this causes multiple comparison problems and the p-values and confidence ...
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13answers
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Bayesian and frequentist reasoning in plain English
How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?
