User rasmodius

11 votes

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Intuitive explanation of Friedman's H-statistic

answered Feb 28, 2021 at 23:02

8 votes

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Bayesian updating with continuous prior in continuous time

answered Mar 9, 2021 at 11:45

6 votes

Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?

answered Mar 1, 2021 at 15:05

6 votes

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Why do we need Gumbel distribution?

answered Jan 21, 2021 at 23:06

5 votes

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the kernel trick and linear regression

answered Feb 22, 2021 at 17:46

3 votes

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Updating posterior probability as more data is given

answered Sep 30, 2019 at 16:58

3 votes

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Are there some measures of information content of Bayesian evidence?

answered Sep 30, 2019 at 20:04

3 votes

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Mean and variance of the Gaussian resulting from Central Limit Theorem

answered Feb 24, 2021 at 14:49

2 votes

Alternatives to Cluster Analysis with Very Large Data

answered Feb 21, 2021 at 1:18

2 votes

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Determine probability of an event using maximum likelihood estimation

answered Mar 3, 2021 at 14:54

2 votes

conditional probability poisson and exponential

answered Oct 1, 2019 at 21:17

2 votes

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Finding standard deviation of formula with three variables

answered Feb 22, 2021 at 18:11

2 votes

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Best approach for energy demand forecasting

answered Feb 20, 2021 at 21:49

1 vote

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Grid Search using strategy

answered Feb 21, 2021 at 0:50

1 vote

Calculating an aggregated score

answered Feb 18, 2021 at 19:59

1 vote

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Why do we assign probability to theta even though we consider it constant in frequentist statistics?

answered Feb 20, 2021 at 17:08

1 vote

Logistic regression for learning curve analysis - two trends

answered Jan 21, 2021 at 14:23

1 vote

Assessing required $n$ for study: Observational unit and observations

answered Jan 21, 2021 at 14:48

1 vote

Standard deviation of a scaled Poisson process

answered Oct 1, 2019 at 10:36

1 vote

why can two random variables be added only when they have the same domain?

answered Sep 30, 2019 at 16:36

1 vote

Finding the posterior with a uniform prior

answered Mar 18, 2021 at 18:23

0 votes

The Monty Hall Problem - where does our intuition fail us?

answered Mar 8, 2021 at 14:59

0 votes

How can I determine how often an event occurs based on collected data how long one has to wait for an occurence

answered Mar 1, 2021 at 15:43

0 votes

Generate data with a given covariance matrix and given non-normal distribution

answered Sep 30, 2019 at 11:13

0 votes

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How to derive prediction equations for classification in a bayesian setting?

answered Jan 21, 2021 at 15:08

0 votes

Bayesian Regression- Expectation Maximization

answered Jan 22, 2021 at 2:15

Stack Exchange Network

26 Answers

Intuitive explanation of Friedman's H-statistic

Bayesian updating with continuous prior in continuous time

Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?

Why do we need Gumbel distribution?

the kernel trick and linear regression

Updating posterior probability as more data is given

Are there some measures of information content of Bayesian evidence?

Mean and variance of the Gaussian resulting from Central Limit Theorem

Alternatives to Cluster Analysis with Very Large Data

Determine probability of an event using maximum likelihood estimation

conditional probability poisson and exponential

Finding standard deviation of formula with three variables

Best approach for energy demand forecasting

Grid Search using strategy

Calculating an aggregated score

Why do we assign probability to theta even though we consider it constant in frequentist statistics?

Logistic regression for learning curve analysis - two trends

Assessing required $n$ for study: Observational unit and observations

Standard deviation of a scaled Poisson process

why can two random variables be added only when they have the same domain?

Finding the posterior with a uniform prior

The Monty Hall Problem - where does our intuition fail us?

How can I determine how often an event occurs based on collected data how long one has to wait for an occurence

Generate data with a given covariance matrix and given non-normal distribution

How to derive prediction equations for classification in a bayesian setting?

Bayesian Regression- Expectation Maximization