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17 votes

Anconda R version - How to upgrade to 4.0 and later

You need to create a new environment and then you can install R 4.+ in Anaconda. Follow these steps. conda create --name r4-base After activating ...
Rheatey Bash's user avatar
9 votes

Why does PCA assume Gaussian Distribution?

TL;DR PCA does assume normal distribution of features See p.55 SAS book1 or Rummel, 19702 or Mardia, 19793. If you expect the PCs to be independent, then PCA might fail to live to your expectations. ...
Oren Milman's user avatar
8 votes
Accepted

Working with Data which is not Normal/Gaussian

There are models that do not make assumption that the underlying data distribution is a normal distribution. For example, support vector machine just cares about the boundaries of the separating ...
Siong Thye Goh's user avatar
8 votes
Accepted

Gaussian Mixture Models as a classifier?

Some unsupervised models can make predictions, but not ones that necessarily match the original class labels. Once a GaussianMixture model has been fitted, it can ...
timleathart's user avatar
  • 3,940
5 votes
Accepted

Why Gaussian latent variable (noise) for GAN?

Why people often choose the input to a GAN (z) to be samples from a Gaussian? Generally, for two reasons: (1) mathematical simplicity, (2) working well enough in practice. However, as we explain, ...
Esmailian's user avatar
  • 9,322
4 votes

Fast way of computing covariance matrix of nonstationary kernel in Python

I still would apply numpy's covaranice function using numpy.apply_along_axis ...
Brian Spiering's user avatar
4 votes
Accepted

Gaussian Mixture Models EM algorithm use average log likelihood to test convergence

It makes unit testing easier; invariant to the size of the sample. Reference: the github discussion that led to the change.
Emre's user avatar
  • 10.5k
4 votes

Why does PCA assume Gaussian Distribution?

Someone correct me if I'm wrong, but the PCA process itself doesn't assume anything about the distribution of your data. The PCA algorithm is simple - find the direction of greatest variance in ...
tom's user avatar
  • 2,248
4 votes

why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?

I do not think your formulation is correct. What you have described are just conditional distributions for each word in the sentence but not the joint conditional distribution, given a specific class. ...
aranglol's user avatar
  • 2,196
3 votes
Accepted

Classification followed by regression to handle response variable that is usually zero

This sounds entirely reasonable, and the usual name for this structure I have heard for this is just "pipeline" which also applies to other system-feeds-next-system structures - it might also be "...
Neil Slater's user avatar
3 votes
Accepted

Replacing missing value by class conditional mean

Your intuition about 'no effect' is true in some sense. But this replacement may be not the best use of the information you have. The choice of missing value treatment depends on your initial ...
David Dale's user avatar
  • 1,551
3 votes
Accepted

A2C Continuous for Pendulum-v0 working implementation, negation for loss and entropy calculation

Again, the entropy equation coded was this: entropy = K.sum(0.5 * (K.log(2. * np.pi * sigma_sq) + 1.)) which looks different from what's given in the textbook photo above. They are the same after ...
Yuq Wang's user avatar
2 votes

Which Normal distribution a point belongs to?

A common distance measure between a point and a normal distribution is the number of standard deviations away from the mean. In your case: $$d_0 = |x-μ_0|/σ_0 = 15.4$$ $$d_1 = |x-μ_1|/σ_1 = 15.82$$ ...
Felix Pîrvan's user avatar
2 votes
Accepted

GausianNB: Could not convert string to float: 'Thu Apr 16 23:58:58 2015'

To cast that column of your data-frame as type float try: k_train = train['cols_to_use'].astype(float) target = train['final_status'].astype(float) More ...
Tarek's user avatar
  • 724
2 votes

How to check if a data is in gaussian distribution in R or excel?

The Normal distribution is the same as the Gaussian distribution. Its just two names for the same thing. Whatever you do - fit parameters, compute goodness-of-fit, etc - if the documentation says its ...
Spacedman's user avatar
  • 1,992
2 votes

Working with Data which is not Normal/Gaussian

Gaussian models are often used (and maybe sometimes over-used) because of their mathematical convenience (many statistical models can be found as built-in functions, when based on the Gaussian ...
Eskapp's user avatar
  • 446
2 votes

How do i use the Gaussian function with a Naive Bayes Classifier?

You got the Bayes theorem wrong. There should be a plus in the denominator: (child gauss x child prob)/(child gauss x child prob + adult gauss x adult prob) (adult gauss x adult prob)/(child gauss ...
seed's user avatar
  • 131
2 votes
Accepted

Mixture Density Network: determine the parameters of each Gaussian component

Actually, the upper alpha and the upper sigma are not free parameters to be set, they are just used to represent the output activations corresponding to the mixture coefficients and the variances, ...
pythinker's user avatar
  • 1,267
2 votes
Accepted

Where can we find the application of bayes's theorem in Bayesian optimiation with gaussian processing

It is a 49 page long paper, so following observations are based only on a cursory reading. The optimisation is for finding best value of parameters for cost function of machine learning models. ...
hssay's user avatar
  • 1,998
2 votes

Understanding GMM-MMI

Multiclass Discriminative Training of i-vector Language Recognition, this article contains update formulas for mean and covariance (diagonal). First you should obtain parameter estimations using ML (...
skywalker's user avatar
  • 121
2 votes
Accepted

Prove GDA decision boundary is linear

If you expand the terms, you can see that the quadratic terms cancel out. \begin{align} a &= \ln \frac{P(C_0)}{P(C_1)} + \frac12(x - \mu_1)^T\Sigma^{-1}(x - \mu_1) - \frac12(x-\mu_0)^T\Sigma^{-1}(...
Siong Thye Goh's user avatar
2 votes
Accepted

What mu and sigma vector really mean in VAE?

In a variational antoencoder (VAE), the output of the encoder, $\mu$ and $\sigma$, is a deterministic function of input data $x$ (you put $x$ into your encoder neural network, and it generates $\mu$ ...
user12075's user avatar
  • 2,284
2 votes

Gaussian distribution in python without using libraries

You need to sort arr. For example you sort df.Age then apply the function and after plotting you will get a beautiful chart. For example, I used your function and a range from 0 to 99 that is already ...
keiv.fly's user avatar
  • 1,259
2 votes
Accepted

What is a latent space vector?

The latent vector $z$ is just random noise. The most frequent distributions for that noise are uniform: $z \sim U[-1,+1]$ or Gaussian: $z \sim \mathcal{N}(0, 1)$ . I am not aware of any theoretical ...
noe's user avatar
  • 26.9k
2 votes
Accepted

Modifying a distribution by adding in samples incrementally

Calculation of standard deviation on the fly is possible (turn to our brothers at math.stackexchange): https://math.stackexchange.com/questions/198336/how-to-calculate-standard-deviation-with-...
Pieter21's user avatar
  • 1,041
2 votes

Anconda R version - How to upgrade to 4.0 and later

Rstudio does not support Anaconda so you are stuck with the version they provide. Your best option is to install R and RStudio outside Anaconda.
Darren's user avatar
  • 21
2 votes
Accepted

Does standardization result in normal distribution?

No, standardization does not change the shape of the distribution. It centers the distribution by subtracting the mean and scales it by dividing by the standard deviation.
LazyEval's user avatar
  • 156
2 votes

Normalizing data from same variable but different individuals

This is a tricky, common problem. The fundamental issue is that a bias may exist between training and production (deployment) data. This video (at ~minute 19) describes a case where an AI X-ray system ...
lpounng's user avatar
  • 1,094
1 vote

How to create a complex Gaussian random noise with a specific covariance matrix

Might be a bit late, but if you're still looking for an answer, here it is. You can use np.random.multivariate_normal. You need to provide the said covariance ...
learner's user avatar
  • 173
1 vote
Accepted

Can I call this graph as a gaussian?

If you left out the large blue and yellow peaks, then maybe. Otherwise, no. With all three distinct peaks, you might call it a multi-modal Guassian - meaning it is a mixture of three standard ...
n1k31t4's user avatar
  • 14.9k

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