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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Interpreting chi-square statistic values and also scipy.stats.chisquare giving unreasonable value
I have some data from the S&P500 of daily returns. I'm not sure if I can show my graph, as I will be using it in my undergraduate paper, but it looks essentially the same as the histogram here:
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Cluster-Based Anomaly Detection (and PCA)
I have a dataset of user operations (250 types of operations) in a trading platform and my task is to extract features, flagging rules, other insight for fraud prevention/anomaly detection, or some ...
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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 ...
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Gaussian Fitting Amplitude Value
Using the Orange Software (Quasar), I am trying to obtain the height of a spectral peak, which I thought I could do by the amplitude of a fitted Gaussian.
However, when I fit a Gaussian to my data (...
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What are the initialization values in webrtc-vad?
I am trying to understand webrtc-vad, which is a python interface to the WebRTC Voice Activity Detector (VAD).
https://github.com/wiseman/py-webrtcvad
In principle, it uses a Gaussian Mixture Model to ...
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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-...
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How can i get this way to create random data?
I need to create random data using this lines
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KL divergence between two multivariate gaussians where $p$ is $N(\mu, I)$
We know if we try to get $D_{KL}(q||p)$, where $p$ is a standard normal distribution, so mean is 0, variance is the identity matrix, and $q$ is a multivariate normal distribution, it can be calculated ...
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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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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 ...
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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 ...
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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.
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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'...
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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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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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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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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 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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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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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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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 ...
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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:
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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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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 ...
