Questions tagged [multivariate-distribution]

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Predicting a Conditional Distribution

Given a stream of data containing a vector of independant variables $X$ and a dependant variable $Y$, I would like to capture the probability distribution of $Y|X$, such that a synthetic data stream ...
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43 views

Linear transformation from one sample to another

Generate a Sample $\underline{Z_1}$ $\underline{Z_2}\dots \underline{Z_{5000}}$ , while $\underline{Z_i} \sim N_2[(0,0)^T,I_2]$ generate new sample with size of $ n = 5000$ by applying linear ...
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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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Getting mean and covariance matrix for multivariate normal from keras model

I have a dataset that has 6 input features and 5 output features. I want to use a keras sequential model to estimate the mean vector and covariance matrix from any row of input features assuming the ...
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Should I concat multiple stock timeseries datasets into one?

I have several timeseries datasets of stock data, with fundamental indicators. I would like to build a model that selects stocks for buy and hold. I understand that to perform this task I have two ...
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Getting vague results using VAR time series forecasting in python!

Firstly, I am a beginner in this field of Data Science and have tried to implement some time series models for wind speed forecasting. Also, I am aware of the fact that some regression models might ...
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Sequential sampling from Gaussian conditional not working

I'm trying to sequentially sample from a Gaussian Process prior. The problem is that the samples eventually converge to zero or diverge to infinity. I'm using the basic conditionals described e.g. ...
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1answer
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Getting a balanced sample across many variables

Let’s say each element in my population has several attributes. Let’s call then A, B, C, D, E, F. Let’s say, for simplicity, each attribute has 10 values (but could be any number between 2 and 30). ...
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Consolidating multivariate time-series information from many data sets

I am having trouble setting up a problem with regards to time series analysis. I have 30 data sets, where each set corresponds to a certain project. Each project has 7 features, and each feature has ...
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Does Anomaly Detection Algorithm works when the features are not correlated?

I am working on an Anomaly Detection Problem and the algorithm I used is an Autoencoder Multivariate Gaussian. The problem with my data is that it is unlabeled and not correlated. For example, let's ...
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179 views

Can GLM( generalized linear method) handle the collinearity between the predictor variables in a regression-analysis?

I'm a beginner in Machine learning and I've studied that collinearity among the predictor variables of a model is a huge problem since it can lead to unpredictable model behaviour and a large error. ...
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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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What is the dimension of a time trend matrix in a VAR model?

What is the dimension of a time trend matrix in a VAR model? Consider the following VAR model: I was thinking the dimensions is (3x2) where -> 3 rows correspond to three X variables -> 2 columns ...
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How to draw a sample from data set with respect to a given categorical or numerical variable based on given freely chosen distribution? (Python)

Say I have a data set for some past period. Now new data appears and for a given variable in the data and we find that the distributions have shifted (for example with "age" it would be that suddenly ...
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How can I fix regression model interpretation of feature?

I'm building a regression model to predict the values of a feature $Y$ given a set of other features $X_{1}, X_{2}, X_{3}..X_{n}$. Onde of these other features, let's say $X_1$, is known to be ...
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Hidden Markov Model with Autoregressive emission model?

So far, all standard HMM implementations I've seen assume some variation of a Gaussian Mixture (GMM) as their emission model. It can of course only have a single mixture component which reduces it to ...
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Can the dependency between variables be deduced from data? And if so, how?

I have a data set $X$ that consists of $m$ vectors $\vec{x}$ of $n$ real-valued components. Each vector component lies within a corresponding predefined interval of valid values, which is the same for ...
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Given a dataset how do i determine the number of modes in the underlying distribution?

given some data is there a technique determine the number of modes (Unimodal distribution, bimodal distribution etc.) without plotting and visualization?
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LSTM features classification output

I am very new at this, so I might be wrong about my choice of model, but my problem is the following. I am trying to generate music, hence the reason I am using an LSTM. I have the following sequence ...
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How does scatter plot with multiple features work?

Plotting one independent(100 rows) and one dependent(100 rows) feature is straightforward. But how plotting multiple independent(100 rows, 10 columns) with one dependent(100 rows, 1 column) feature ...
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How to interpret Correlation along with Coefficients of multiple linear regression ?£

I have 10000 samples. There are 4 independent variables and 1 dependent variable. The independent variables are all centered with 0 mean. I found the correlation coefficients between each of these ...
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102 views

How to resample one dataset to conform to the distribution of another dataset?

I have two datasets with 20 features, but with different feature distributions (DS_A and DS_B). How can I sample the DS_A to make its distribution similar to DS_B, with respect to multiple features?? ...
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Are there any methods to detect whole multivariate time-series as anomalous from a set of multivariate time-series?

Consider a scenario with Dataset D as {T1, T2, ..., Tn} and Ti is a multivariate time-series of length mi as {X1, X2, ..., Xmi}. Here each record of the time-series Xi is a vector of attribute values {...
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How to do Multivariate Adaptive Regression Splines feature selection in python? Specifically, I need the python equivalent of the earth function in R

This is the code in R: marsModel <- earth(eval(parse(text=paste(ResponseVariable,"~."))), data = data) #build model ev <- evimp (marsModel) Response ...
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Confidence intervals in multivariate linear regression

I am fitting my data to a multivariate linear regression $Y = BX + \Xi$, where the response is bivariate $Y\in R^{n\times 2}$, and the predictor is uni-variate but elevated to the projective plane to ...
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47 views

Select the right distribution

I have a dataset like: ...
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Sampling trying to keep as much multivariate variance as possible

I was thinking if anyone considered a sampling technique that would try to aim keeping as much of the variance as possible (e.g. as many unique values, or very widely distributed continuous variables)....