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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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GMM expectation maximization ValueError: could not broadcast input array from shape (2700,1) into shape (2700,)

Problem Statement The goal is to have the Gaussian Mixture Model has_prediction() function to execute after the Gaussian Mixture Model fix() function that will invoke methods on this custom ...
Data Science Analytics Manager's user avatar
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Two overlap gaussian mixtures come from one or two generations of data?

Reviewing for a ML exam, my teacher asked last year a philosophical question about gaussians and generations of data which I couldn’t find no good answer. Imagine that an oracle can say with 100% ...
Adrian Lara's user avatar
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Efficiently cluster Gaussian vectors

Let $X^i,1\leq i\leq n$ iid Gaussian vectors of $\mathbb{R}^p$. I want to cluster the coordinates $1\leq j\leq p$ knowing the following facts: Each coordinate $X^i_j$ is centred and has variance $1$, ...
kaleidoscop's user avatar
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Question regarding Lecture 5 CS229

I was at the part where we are using one covariance matrix and two mean vectors for fitting our Gaussian. I understood that we are using one covariance matrix and we can use different ones that would ...
Kshitij Singh's user avatar
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Implementing Complement Naive Bayes for continuous data?

I'm looking to implement the Complement Naive Bayes algorithm for my continuous features. For the Gaussian technique I have calculated the normal distribution per feature x class, however what's the ...
litterbugkid's user avatar
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Gaussian 'around' a given distribution

I want to find the data points within 0.2 dex of the Main Sequence Relation: (The main sequence relation is what you can observe in this figure) Dex is an astronomical jargon which signifies scatter, ...
Ambica Govind's user avatar
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Investigating the Impact of Additive Gaussian Noise on EEG Signal Classification: Analyzing the Relationship between Augmented and Original Data

Definition: I have conducted research on EEG signal classification, specifically focusing on distinguishing between two different classes using raw EEG signals. Data availability poses a significant ...
Armin Amini's user avatar
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Expectation Step in Gaussian Mixture Model for Matrix Data Not Producing Proper Posterior Probabilities

I'm working on implementing a Gaussian Mixture Model (GMM) for three-way data (i.e., a set of matrices) in R. The GMM is being estimated using the Expectation-Maximization (EM) algorithm. However, I'm ...
John Smith's user avatar
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Math of Gaussian Mixture Models & EM Algorithm

I'm having a hard time understanding the math behind GMMs. A GMM is a weighted sum over K different Gaussian components with parameters $\mu_k, \sigma_k, \pi_k$ From my understanding, the general ...
bosonphoton's user avatar
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I have a question about the DDPM equation derivation related to the multiplication and division of Gaussian distributions

The equation involves multiplying two Gaussian distributions and dividing by another Gaussian distribution, which cannot be solved using the method of multiplying and dividing probability density ...
user148504's user avatar
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1 answer
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Knowing the standard deviation of 2 normal distribution

I'm learning linear algebra for data science on some site. There is a quiz and the result I've answered that I linked to the image. I'm curious about why the standard deviation part is wrong? As I ...
Rickyslash's user avatar
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Understanding the plate notation for gaussian mixture models and latent dirichlet allocation

I am having troubles understanding the plate notation being used in LDA and GMM. In specific the class-variable deciding which parameters that generates the observation in GMM's and the topic-...
richard's user avatar
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How can i get this way to create random data?

I need to create random data using this lines ...
user20133451's user avatar
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Using scipy.minimize to find the maximum likelihood estimates for multivariate gaussian

Let's say I have a 100x2 normally distributed array of data. ...
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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 ...
Benisburgers's user avatar
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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. ...
Alpha code 's user avatar
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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 ...
Arzental's user avatar
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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 ...
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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 ...
ItDepends's user avatar
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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) the state of the model at each time period (with continuous observations). To maximize the likelihood of the model ...
Lilit's user avatar
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"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 ...
Cortexelus's user avatar
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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, ...
Jorge A. Salazar's user avatar
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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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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'...
Bar Benezri's user avatar
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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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How did the author got this final result from this Gaussian Distribution formula? [closed]

How did they got the final result?
learner's user avatar
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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 ...
rb173's user avatar
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1 answer
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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 ...
Scott's user avatar
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2 votes
1 answer
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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 ...
JackEarl's user avatar
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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 ...
scarlett rouge's user avatar
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25 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/...
Just_Alex's user avatar
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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 ...
AntonYellow's user avatar
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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 ...
MrRobot9's user avatar
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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 ...
NewDev's user avatar
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18 votes
5 answers
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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 ...
Saranya Prakash's user avatar
3 votes
1 answer
169 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?
star's user avatar
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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 ...
hsiaomichiu's user avatar
1 vote
2 answers
60 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 ...
Veronica's user avatar
2 votes
1 answer
226 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 ...
star's user avatar
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2 votes
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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 ...
MJimitater's user avatar
2 votes
1 answer
171 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 ...
Naveen Gabriel's user avatar
4 votes
1 answer
105 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 ...
Arnold's user avatar
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2 votes
1 answer
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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 ...
Tarun Gupta's user avatar
1 vote
1 answer
687 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 ...
NakedCat's user avatar
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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 ...
Jericho Jones's user avatar
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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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4 votes
2 answers
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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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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 ...
Mario's user avatar
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1 vote
1 answer
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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: ...
mLstudent33's user avatar
1 vote
1 answer
2k 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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