Clemens Haerder
  • Member for 2 years, 6 months
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  • Zürich, Schweiz
Why sometimes evidence is said to be too complex to compute and other times is negelected cause it is fixed?
1 votes

If you check Bayes` Theorem the denominator is just used as a normalizing constant that ensures that in the continuous case the posterior density integrates to 1. And it is easy to show that the ...

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Can Park & Casella's Bayesian LASSO be applied to generalized linear models?
Accepted answer
1 votes

Here my thoughts. Hope you already found the solution as I am sort of at the same point. Changing the Likelihood of y for GLM changes the full conditional of your betas and likely the variance. So I ...

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Day-of-week effects on regression coefficients in autoregressive model?
0 votes

I think you have two options with option one using a SAR(IMA) model. If you have a reoccurring effect every weekday (dummy), then you can already improve the model. Option 2: As you stated - I would ...

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How to score/measure the change in a time series from t-x,...t-1 to t0 (Relative change)
0 votes

To capture the relative effect i would go for x_t - x_t-1 / x_t-1 If you want the same result in your example x_t-1 = 13 x_t = 23 & z_t-1 = 3 z_t = 13 you could just go for the difference x_t - ...

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What is the difference between machine learning approaches and Fourier series to fit a curve to data graph?
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Using Fourier series is machine learning. Machine/Statistical learning is just a general term for all kinds of tasks where learning from data is required.

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PCA explained variance and clustering
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1) The factor map indicates that my two first principal components explain 48% of the variance in the data. I've read that this might be due to that the correlation among variables... PCA does only ...

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What is the common ingredients of a machine learning algorithm?
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You might want to check the definition of "supervised" & "unsupervised" statistical (machine) learning. And most importantly check & understand the methods you want to apply. I wouldn't ...

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