Linked Questions
13 questions linked to/from What exactly is a hyperparameter?
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What do we mean by hyperparameters? [duplicate]
Can anyone give me full details about what we mean by hyperparameters, and what in the Dirichlet distribution are called hyperparameters? A practice example for the estimation of those parameters ...
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What are the hyperparameters? [duplicate]
I find the meaning of hyperparameters not always clear. The hyperparameters are defined as "the parameters of the prior". Suppose that one has prior information about a certain parameter $\theta$. ...
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what is a parameter and hyperprameter [duplicate]
I hear in many articles the word parameters and hyperparameters but I don't know what they mean by that.
Are they variable or the weights of the nodes?
explain me in an intuitive way as an analogy ...
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Bayes regression: how is it done in comparison to standard regression?
I got some questions about the Bayesian regression:
Given a standard regression as $y = \beta_0 + \beta_1 x + \varepsilon$.
If I want to change this into a Bayesian regression, do I need prior ...
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What's in a name: hyperparameters
So in a normal distribution, we have two parameters: mean $\mu$ and variance $\sigma^2$. In the book Pattern Recognition and Machine Learning, there suddenly appears a hyperparameter $\lambda$ in the ...
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15
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When to stop refining a model?
I have been studying statistics from many books for the last 3 years, and thanks to this site I learned a lot. Nevertheless one fundamental question still remains unanswered for me. It may have a very ...
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Estimation of Bayesian Ridge Regression
According to scikit-learn, by using a probabilistic model :
$p(y|X,\omega,\alpha) = \mathcal{N}(y|X\omega,\alpha)$
with $\omega$ given by a spherical Gaussian:
$p(\omega|\lambda) = \mathcal{N}(\...
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What are the model parameters and hyperparameters of Random Forest classifier?
The parameters required for a Random Forest classifier are as follows:
Depth, $d$
No. of random features, $K$
No. of trees, $I$
Randomizer seed, $R$
Which of the above are hyperparameters and which ...
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Is it correct to do hyperparameter tuning based on validation error and not test error?
I am new in Statistics and Data Science.
I would like to use "academic" data for training and testing for overfitting.
However, I would like to get the classifier accuracy from "real-world" data and ...
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2
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How to use priors on the parameter number with an information criterion (AIC, BIC, …)?
Example
The example is made up because I hope that it’s more accessible than my actual problem.
I want to determine the number of planets of a star.
I have:
data for some astronomical observable of ...
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0
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2
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Monte Carlo simulation or big samples
Suppose that we have a population that behaves as $N(1,\sigma^2)$, and we want to estimate $\sigma^2$. I would like to understand the difference between these two approaches:
We take a sample $x_1,\...
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Selecting Bayesian priors for the dependent data
I have goal of measuring CTRs of several titles of an article on a website using Bayesian approach.
In a simple setup, what one will do is to select Beta Prior (for example Uniform distribution) and ...
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Type checking the Bayes formula
In the Bayes formula,
$$
p(\theta|D) = \frac{ p(D|\theta) p(\theta) }{ p(D) }
$$
which of $D, \theta$ should we regard as given and which as
variables,
which of the factors $p(\theta|D), p(D|\...
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