Questions tagged [gaussian]

Gaussian refers to the Gaussian (or Normal) distribution. This is a continuous probability density function and is widely used in statistics.

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Using numpy to enter noise into data

I am new to data science and have to generate 200 numbers from a uniform distribution set this as x and generate y data using x and injecting noise from the gaussian distribution y = 12x-4 + noise ...
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Checking if an image has an noise in it or not using psnr signal value

I basically want to check if an original image has noise in it or not. To do this, I came up with an approach where the original image is filtered first like using Gaussian filter. And then I ...
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How to improve my f1 score in stories analyze

I got an assignment to build a model that identify the gender of the text writer. The assignment score will determine by my model f1_score, to get the maximum points, T need it will be at least 0.7. I'...
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Fitting input data into Gaussian distribution

I'm currently reading papers on Variational Autoencoders (VAE). According to this article (http://proceedings.mlr.press/v95/guo18a/guo18a.pdf): By fitting the input data sample x(i) into the Gaussian ...
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38 views

How to generate training instances from multivariate normal distribution? [closed]

I would like to generate a training and test set for the binary classification problem. I want the instances to be generated from a multivariate normal distribution and have 5 features. Can someone ...
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27 views

Gaussian Mixture Implementation and Optical Recognition of Handwritten Digits Data Set

Trying to implement Gaussian Mixture model implementation in python using the Optical Recognition of Handwritten Digits Data Set which consists of 10 training folds each of size $\left[100x64\right]$, ...
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Gaussian Mixture Classification Implementation with multidimensional trainning data

I'm trying to implement the gaussian mixture classification (GMC) implementation from scratch using python. The training dataset consists of 10 folds each of size $\left[100x64\right]$. In addition, ...
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1answer
68 views

Similarity between 2 statistical distributions

Is there any index that measures similarity between 2 gaussian distributions of 1-D data (may have slightly different number of points) considering their mean shift, variance shift, difference in ...
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12 views

Robust Gaussian Fit

I have tried to find some literature on robust gaussian fits, all I could find was good old EM gaussian mixtures. The question is : given a mixture of gaussians, find the dominant one around a given ...
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67 views

Extracting component means and convariances from mixture model

I am currently trying to write a simple multivariate gaussian mixture model using tensorflow probability. Specifically, I have some 2-dimensional input and 2-dimensional output data and am looking to ...
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1answer
32 views

what are the main differences between parametric and non-parametric machine learning algorithms?

I am interested in parametric and non-parametric machine learning algorithms, their advantages and disadvantages and also their main differences regarding computational complexities. In particular I ...
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21 views

Does standardization result in normal distribution?

I have a question about standardization (subtract mean, divide by standard deviation) of data consisting of different features with different ranges. I read some information that seemed to be ...
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15 views

Multivariate noise variance in Gaussian process prediction

In GP regression, we predict using $\mu^* = ... (K(X,X)+\sigma^2I)^{-1}...$ This is fine when the noise $\sigma$ is a scalar, but I am confused about what happens when $\sigma$ is Multivariate/...
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Basis Function Regression - Providing analytic expression - What to do with implicitly defined parameters?

within the probability distribution of p(y|x,w,c,d,a) c and d are implicitly defined within f(x). For a) I would have suggested: p(y | x,w,c,d,a) = N(y | f(x),a) My question: Is it okay to ignore c,d ...
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Sampling for the encoder part of the VAE

my question regards the code utilized to implement the sampling function in the encoder part of VAE. Supposing that we chose a latent dimension of 2. Before the latent representation, we have 4 ...
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43 views

Practical difference between Bayesian Neural Networks and Feed Forward Neural Network with Gaussian Noise

To my understanding, a BNN's weights come from a Gaussian with trained mean and standard deviation, while a FFNN of the following form, comes from a learned weight, which acts as a 'mean', and is ...
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2answers
144 views

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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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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25 views

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 ...
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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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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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1answer
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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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1answer
56 views

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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1answer
34 views

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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1answer
225 views

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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1answer
157 views

What is a latent space vector?

I do not understand this about GANs. Apparently the Generator is supposed to receive a latent space vector as its input. Yet I couldn't find an example of how I can implement it in Pytorch. This is a ...
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1answer
90 views

Co-joining multi-peak histograms

I am analysing a bunch of data files which represent responsiveness of cells to addition of a drug. If a drug is not added, cell responds normally, if it is added, it shows abnormal patterns: , . We ...
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1answer
34 views

What is a “variable index” in the Gaussian perspective?

I was going through this article about Gaussian processes, in which the author explains about the "variable index" in the form of a plot while writing about 2D Gaussian. The explanation and plot are ...
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2answers
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Is it possible to train probabilistic model to return several distributions?

I have nonlinear data of function y(x), which is let's say parabolic. At some points of x there are several y's (look at the picture). Is it possible to train a probabilistic model to return several ...
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68 views

How can detect and highlight outliers by using gaussian function and normalize the data elegantly?

I tried to normalize the data by using Gaussian function 2 times on both positive and negative numbers of each parameter of this dataset. The dataset includes missing data as well. The problem is I ...
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1answer
689 views

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

very good implementation of A2C continuous for Pendulum-v0 Code has snippet to stop execution when mean of last 10 or 20 is higher than -20 but the results look like: ...
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1answer
808 views

Prove GDA decision boundary is linear

My attempt: (a) I solved that $a=\ln{\frac{P(X|C_0)P(C_0)}{P(X|C_1)P(C_1)}}$ (b) Here is where I'm running into trouble. I'm plugging the distributions into $\ln{\frac{P(X|C_0)P(C_0)}{P(X|C_1)P(C_1)...
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How is the standard deviation of VAE's obtained?

I am trying to build a Variational Autoencoder. I was looking at various codes online and found most of them in some way or another copy Francois Chollet (Google researchers) code. Now my main ...
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Why Gaussian latent variable (noise) for GAN?

When I was reading about GAN, the thing I don't understand is why people often choose the input to a GAN (z) to be samples from a Gaussian? - and then are there also potential problems associated with ...
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1answer
888 views

Fast way of computing covariance matrix of nonstationary kernel in Python

Suppose I have symmetric positive definite covariance function $k:\mathbb{R}\times\mathbb{R}\rightarrow \mathbb{R}$ that is non-stationary (i.e. $k(x,y) \neq g(|x-y|)$ for any function $g$). Is there ...
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Gaussian Mixture Models as a classifier?

I'm learning the GMM clustering algorithm. I don't understand how it can used as a classifier. Here are my thought: 1) GMM is an unsupervised ML algorithm. At least that's how ...
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95 views

Deepmind conditional neural process: evaluation

Going through the Deepmind jupyter notebook conditional neural processes, the plots at the bottom of the notebook show that the ground truth and the predicted distribution only overlap around the "...
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1answer
903 views

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

I am trying to generate a complex Gaussian white noise, with zero mean and the covariance matrix of them is going to be a specific matrix which is assumed to be given. Assume i to be a point on the ...
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24 views

How to use Adaptive Rejection Sampling to Update Alpha in iGMM

I am wondering how can I use ARS (adaptive rejection sampling) in dirichlet process mixture model (infinite Gaussian mixture model) as described by Rasmussen (https://www.seas.harvard.edu/courses/...
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Can I call this graph as a gaussian? [closed]

My program is a chatbot. It has rule to represent the state that user is talking to the bot at node level n. I have 1 to 9 nodes in the application. Here is the ...
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1answer
6k views

Gaussian distribution in python without using libraries

I am implementing Gaussian distribution of a variable, but it gives multiple bell shapes. It should be a single bell shape. Below is my code and plot. ...
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1answer
239 views

Probabilistic Outlier Detection (edited + clarified)

Measured data $D \in \mathbb{R}^3$, every $d^i \in D$ is $d^i_{(x)}$, where the $x=[x_1, x_2]$. Simply said, the measured data are function of $x$. It is known, the dependency is linear, such as: $$...
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What are the reasons for drawing initial neural network weights from the Gaussian distribution?

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, ...
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Parameter of Conditional Gaussian Distribution

I'd like to understand how to determine the parameter of conditional gaussian distribution. Following is the network architecture of VUNET which learns the conditional gaussian distribution $q(z|x, \...