Questions tagged [multivariate-distribution]
The multivariate-distribution tag has no usage guidance.
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Adressing uncertainty of a spatio-temporal multivariate timeseries with random temporal gaps
Imagine there are multiple locations of interest from where water samples are gathered manually. Each sample is immediately analyzed, converted to a numerical value (a real number) and fed into a ...
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Fit a bimodal histogram with a mixture Model that is not Gaussian
I am attempting to fit with Python a histogram which presents a bimodal distribution. For now, I have tested to use the Gaussian Mixture Model (GMM) from Scikit-Learn, but I want to try with different ...
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Generate new sample with higher variance, covariance from empirical distribution
I am analyzing the joint distribution of three continuous variables. I would like to sample from the joint distribution of these three variables, which is straight forward. However, I also want to ...
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Python Library Trend time series multivariate
Our csv contains 36 columns
1 date time column collected every 30 mins
3 variables (count,latency,Totaltime) x 10 Features(user io, serverio ,concurrency ..etc ) Of different data points from the ...
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Critique my algorithm for measuring similar/difference of groups using multiple variables
So I've been trying to solve a problem of quantitatively measuring the similarity/difference between groups in my dataset. I am not trying to cluster data to create groups, because the groups are ...
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Creating a dataframe using roll-forward window on multivariate time series
Based on the simplifed sample dataframe
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How to calculate the KL divergence for two multivariate pandas dataframes
I am training a Gaussian-Process model iteratively. In each iteration, a new sample is added to the training dataset (Pandas DataFrame), and the model is re-trained and evaluated. Each row of the ...
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Why does the PCA Scores plot (PC1 vs PC2) flips when using extracted variables from the Gaussian?
Imagine you have some multivariate data (1000s of variables) which approximately follows the Gaussian distribution.
You can generate various PCA Scores plots from this data, of course. One option is ...
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Predicting children growth
I am doing a project where I am supposed to forecast future athletes' performance one, two, three, etc. years in the future. The dataset consists of athletes' scores on tests done from they were kids ...
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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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Kullback Leibler divergence of two datasets having different sizes
is there a way to apply KL on 2 datasets of different sizes?
ps: my datasets are 2 heat maps with 120x180 and 60x90 as width and height respectively.
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Forecasting on multivariate time series containing quaternions
I have a multivariate time series containing 3D position data ($x,y,z)$ and orientation data (as quaternions) obtained from motion sensors.
My goal is to forecast the future position/orientation, and ...
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Which dataset for multivariate time series forecasting
I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ?
Can I use a dataset ...
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141
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regarding computing the centroid of high dimensional data
In scikit-learn, or other python libraries, are there any existing implementations to compute centroid for high dimensional data sets?
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Multivariate data preprocessing
I am trying to understand how multivariate data preprocessing works but there are some questions in my mind.
For example, I can do data smoothing, transformation (box-cox, differentiation), noise ...
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Meaning of the covariance matrix?
I wonder about the excessive usage of the covariance matrix across all kinds of machine learning tools. So far, for me, the covariance is just a pre-step to get to the correlation. And as there is an ...
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MLE for Poisson conditioned on multivariate Gaussian?
I am writing some Python code to fit 2D Gaussians to fluorescent emitters on a dark background to determine the subpixel-resolution (x, y) position of the fluorescent emitter. The crude, pixel-...
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How to build multiple variable regression having a mix of numerical & categorical features?
There is a need to estimate Annual Average Daily Traffic Volume (AADT).
We have bunch of data about vehicles' speeds during several years. It is noticed that AADT depends on the average number of such ...
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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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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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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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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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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 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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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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229
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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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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)....
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