Questions tagged [gaussian]

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

Filter by
Sorted by
Tagged with
1 vote
1 answer
80 views

Using scipy.minimize to find the maximum likelihood estimates for multivariate gaussian

Let's say I have a 100x2 normally distributed array of data. ...
  • 31
0 votes
0 answers
7 views

How to combine data measurements with underlying model?

The problem I am trying to solve is this: Imagine you are in the situation where you want to predict car performance according to some characteristics (horse power, car dimensions, etc...). The data ...
0 votes
1 answer
19 views

Normalizing data from same variable but different individuals

I'm new to machine learning. I have the following scenario: I have five individuals that are each carrying an accelerometer. That sensor measures movement/acceleration on a scale from 0 to 255, 0 ...
1 vote
1 answer
23 views

Confused on Naive Bayes classifier

In the last part of Andrew Ng's lectures about Gaussian Discriminant Analysis and Naive Bayes Classifier, I am confused as to how Andrew Ng derived $(2^n) - 1$ features for Naive Bayes Classifier. ...
1 vote
0 answers
16 views

My data can be approximated with Normal mixture. How can I find the reasons and explain this behaviour?

I use DeLonge method to compare two ROC AUCS. The result of it is Z-score. Both ROC AUCs obtained from LDA (linear discriminant analysis) from sklearn package. The ...
0 votes
0 answers
16 views

Is there a Gaussian Mixture Model for data with opposing pairs?

I have a classification problem with data that comes in pairs. A pair consists of two datapoints (A,B) or (B,A), each datapoint containing 20 features. After receiving about 30 pairs, my goal is to ...
0 votes
2 answers
689 views

Comparing distributions Python

I have a larger dataset (random variable) 'x' containing values approximating a Gaussian distribution. From 'x', a much smaller random variable 'y' is sampled without replacement. I want to compare ...
  • 121
0 votes
0 answers
44 views

Sampling from Discrete Space using Latin Hypercube

I wanted to use Discrete Latin Hypercube to sample 20 samples from a space created by an array that has 4096 values. So my desired output would be something like the following: ...
1 vote
0 answers
15 views

t-SNE - how variance is set and how it affects dense vs sparse clusters in HD space

When learning about t-SNE, I found a resource saying "width of the normal curve (a gaussian centered at $x_i$) depends on the density of data near the point of interest". Which is why we do ...
1 vote
0 answers
92 views

Likelihood of a hidden Markov model does not increase in case of multidimensional data

I am implementing a HMM (Hidden Markov Model) for time series data to assess (define) state of the model at each time period (with continuous observations). To maximize the likelihood of the model (...
  • 11
0 votes
0 answers
27 views

Understanding forward process in diffusion models

I was reading a blog on diffusion models where I came across this expression. I didn't understand why it is \begin{align} \sqrt[]{1-\beta \small{t}}*\large{x}\small{t-1} \end{align} and what exactly ...
  • 1
0 votes
1 answer
70 views

"Up or down but not sideways" bimodal time series prediction - what is the best way to model it?

Say I have a time series (e.g. bitcoin price). I want to predict tomorrow's price, specifically tomorrow's % change in price from today. Let's say this is gaussian distributed, with the mean at 0%. If ...
0 votes
1 answer
57 views

2 Most probable labels with Gaussian Mixture Model Clustering

I want to get the two most probable labels for each sample in my X. A little context: I am working on a clustering project where I have 1.6M samples that have to be clustered into 12 clusters. First, ...
0 votes
1 answer
214 views

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 ...
  • 107
1 vote
1 answer
26 views

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'...
1 vote
1 answer
99 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 ...
  • 21
0 votes
1 answer
32 views

How did the author got this final result from this Gaussian Distribution formula? [closed]

How did they got the final result?
  • 23
1 vote
1 answer
257 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 ...
  • 71
1 vote
1 answer
113 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 ...
  • 11
2 votes
1 answer
50 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 ...
0 votes
1 answer
37 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 ...
1 vote
0 answers
18 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/...
  • 111
1 vote
0 answers
74 views

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 ...
0 votes
2 answers
526 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 ...
  • 139
1 vote
1 answer
54 views

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 ...
  • 155
14 votes
5 answers
37k views

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 ...
3 votes
1 answer
80 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?
  • 1,261
0 votes
2 answers
38 views

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 ...
1 vote
2 answers
34 views

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 ...
2 votes
1 answer
108 views

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 ...
  • 1,261
2 votes
1 answer
37 views

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 ...
2 votes
1 answer
104 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 ...
4 votes
1 answer
45 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 ...
  • 43
1 vote
1 answer
787 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 ...
1 vote
1 answer
340 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 ...
  • 113
2 votes
1 answer
113 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 ...
0 votes
1 answer
40 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 ...
  • 8,076
4 votes
2 answers
51 views

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 ...
  • 141
1 vote
0 answers
114 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 ...
  • 389
1 vote
1 answer
878 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: ...
1 vote
1 answer
1k 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)...
  • 63
1 vote
0 answers
65 views

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 ...
  • 763
10 votes
1 answer
2k views

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 ...
2 votes
1 answer
1k 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 ...
7 votes
1 answer
5k views

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 ...
  • 173
1 vote
0 answers
99 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 "...
  • 203
1 vote
1 answer
2k 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 ...
  • 111
1 vote
0 answers
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/...
-1 votes
1 answer
54 views

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 ...
  • 111
0 votes
1 answer
7k 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. ...