# Questions tagged [gaussian]

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### CNN: How do I handle Blurred images in the dataset?

I have 30% blurred images in each classes. I have a total of 10 classes. I'm not allowed to drop these blurred images. How do I train the model to get better accuracy for both blurred and nonblurred ...
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### does gaussian distribution of the data features stimulate us to apply PCA? [closed]

before applying PCA on data, I plotted the distribution of each feature. my data is 5 face landmarks each landmark has 2 coordinates which result in 10 features the distribution of all the features ...
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### Calculating probability from a distribution

I have a histogram/count plots of average time a property is in the market. Given this distribution, I'd like to calculate the probability that a similar property will be leased within 'x # of weeks' ...
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### Kernel approximation of a function known only point-wise?

Assume that I have a set of $N$ points $x_i, i=1,...,N,$ in some space $\mathbb{R}^D$, and corresponding point-wise (scalar) function evaluations $f(x_i)$. It is my goal to approximate the unknown ...
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### Multivariate Gaussian distribution - Covariance vs linear dependence

From prof. Andrew Ng's Multivariate Gaussian distribution lecture, covariance measures linear dependency between features, in which case we might use Multivariate Gaussian distribution with covariance ...
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### Anconda R version - How to upgrade to 4.0 and later

I use R through the anaconda navigator, which manages all my package installations. I need to use qgraph for a project, which is dependent on mnormt library, which in turn needs RStudio verion >4.0 ...
47 views

### Why Gaussian mixture model uses Expectation maximization instead of Gradient descent?

Why Gaussian mixture model uses Expectation maximization instead of Gradient descent? What other models uses Expectation maximization to find best optimal parameters instead of using gradient descent?
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### what are Latent model and Latent Varibles?

what are latent models and why they use Expectation maximization instead of gradient descent to optmize the parameters ? How to interpret and understand whether a hidden varible is present in model or ...
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### what kind of distribution is followed by word and sentence vectors generated by TFIDF ,word2vec,glove,bert,flair?

what kind of distribution is followed by word or sentence embedding vectors generated by TFID or pretrained models like word2vec,glove,bert,flair ? is it continuous or discrete or any other ...
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### Computing generalized variance for high-dimensional data

What are some practical methods to compute the generalized variance $|\Sigma|$ of a multivariate Gaussian, for high-dimensional data (d>200)? I'm having troubles computing the product of eigenvalues ...
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### How to create anisotropic exponential and gaussian correlation function in Python for kernel?

I have a dataset of 1000 observed samples of 6 features that form the X and one target variable that forms the Y. I am using kriging or Gaussian Process Regressor to train my models. I would like to ...
28 views

### Question regarding Gaussian window function using pandas

I'm trying to aggregate my timeseries data using a rolling window. Within a particular window, I would like to use a bell curve to assign weights to individual data points. To be more specific: the ...
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### Use a single Gaussian to represent a mixture of Gaussians

I want to merge a Gaussian mixture, $\sum_{i=1}^{K} w_i \exp(x; \mu_{i}, \Sigma_{i})$ into one single Gaussian, under the constraints that $w_1 >> w_2 \geq \dots \geq w_K$, i.e. we have a ...
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### How to model anomaly data using Gaussian distribution assuming variables are dependent? (In Python)

I have some data which contains anomalies as well. I want to model data using Gaussian distribution assuming variables are dependent in Python. How can I model this? Should I use the PDF formula as ...
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### why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?

Im trying to understand why naive is needed in Naive Bayes and everyone says Naive Bayes assumes the input features (predictors) are not correlated hence they are not dependent on each other . i want ...
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### What is the “learning” step in Gaussian Naive Bayes classification?

For conditionally independent features $f_i$, Naive Bayes Classification gives me the classifier $Classifier(f) := \arg \max_{k} P(C=k) · ∏^n_{i=1} P(f_i|C=k)$ for classes $k$. I understand that ...
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### GMM in speech recoginition using HMM-GMM

I am trying to solve/understand ASR using HMM-GMM. At the abstract level i do understand what's happening but I did not understand how GMM fits into it. My data has 5K hours of speech from single ...
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### Gaussian mixture models: find correct prior

This task might be pretty simple to the most of you. However, I am struggling to calculate the PDF for the Gaussian distributions. Help is very much appreciated!
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### Modifying a distribution by adding in samples incrementally

I would like to calculate the distribution (e.g., Gaussian) of a set of samples. However, I would also like to see how the distribution changes as I fit the samples into the distribution ...
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### Relationship between Sigmoid and Gaussing Distribution

I was reading this article where I came across the following statement in the context of "Why do we use sigmoid activation function in Neural Nets?": The assumption of a dependent variable to follow ...
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### What are the reasons for drawing initial neural network weights from a Gaussian?

Are there theoretical or empirical reasons for drawing initial weights of a multilayer perceptron from a Gaussian rather than from, say, a Cauchy distribution?
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### What mu and sigma vector really mean in VAE?

In standard autoencoder, we encode data to bottleneck, then decode with using initial input as output to compute loss. We do activate matrix multiplication all over the network and if we are good, ...