Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [noise]

noise is a term used for the error term in statistical models and in signal processing. It could be white noise, colored noise or otherwise.

Wikipedia has an article https://en.wikipedia.org/wiki/Noise_(signal_processing) with further references.

236 questions
20
votes
2 answers

Classification with noisy labels?

I'm trying to train a neural network for classification, but the labels I have are rather noisy (around 30% of the labels are wrong). The cross-entropy loss indeed works, but I was wondering are there any alternatives more effective in this case? or…
dontloo
  • 13,692
  • 7
  • 51
  • 80
16
votes
3 answers

How is adding noise to training data equivalent to regularization?

I've noticed that some people argue that adding noise to training data equivalent to regularizing our predictor parameters. How is this the case? Some of the examples listed on SE discussing this topic focus more on e.g. LSTMs and SVMs, but can we…
Ice Tea
  • 315
  • 8
16
votes
2 answers

Probability distribution for a noisy sine wave

I'm looking to analytically calculate a probability distribution of sampling points from an oscillating function when there is some measurement error. I have already calculated the probability distribution for the "without noise" part (I will put…
Greg
  • 464
  • 3
  • 9
15
votes
2 answers

Principal Component Analysis Eliminate Noise In The Data

Do Principal Component Analysis (PCA) eliminate noise in the data set? If PCA do not eliminate noise in the data set, what actually does PCA do to the data set? Can somebody help me regarding this matter.
bbadyalina
  • 743
  • 2
  • 7
  • 20
13
votes
2 answers

Generate uniform noise from a p-norm ball ($||x||_p \leq r$)

I am trying to write a function which generates uniformly distributed noise which comes from a p-norm ball of $n$ dimensions: \begin{equation} ||x||_p \leq r \end{equation} I found possible solutions for circles ($p = 2$)…
Taeke de Haan
  • 311
  • 1
  • 8
8
votes
4 answers

Relationship between overfitting and robustness to outliers

What's the relationship between overfitting and sensitivity to outliers? For example: Does robustness to outliers make necessarily models less prone to overfitting? What about the other way around? Are models that are less prone to overfitting…
Josh
  • 3,408
  • 4
  • 22
  • 46
6
votes
1 answer

What is the difference between a non-zero nugget and a noise term in Kriging/GPR?

With some Gaussian Process Regression/Kriging models, it's possible to specify both a non-zero nugget, and a noise term. For example, in Scikit-learn's GPR model, there is an alpha parameter, which I think represents the nugget, and a WhiteKernel…
naught101
  • 4,973
  • 1
  • 51
  • 85
6
votes
2 answers

Estimating growth rate from noisy data?

Let's say we want to estimate growth rate from noisy data. Due to noise, simple calculation will result in very poor estimates (calculating growth rates just exacerbates noise), so smoothing is needed. This is quite straightforward up to this point,…
Tamas Ferenci
  • 3,143
  • 16
  • 26
6
votes
1 answer

How i add uniformly distributed noisy attributes to data set?

I want to add some artificial outliers to my data set by follow same method below. so, how i can add contaminated data statistically to real data set like Pima Indians Diabetes? info: Pima Indians Diabetes: 768 instances 8 attributes enter…
user
  • 195
  • 6
6
votes
2 answers

Correcting Kullback-Leibler divergence for size of datasets

We have the following implementation of KLD: import numpy as np import pandas as pd from scipy.stats import entropy def KL_divergence(a, b): hist_a = np.histogram(a, bins=100, range=(0,1.0))[0] hist_b = np.histogram(b, bins=100,…
Hristo Buyukliev
  • 458
  • 4
  • 10
6
votes
2 answers

Estimate the variance of Gaussian distribution from noisy sample

I have measured a large data sample from an underlying Gaussian distribution and want to estimate the variance and its error. However, the measured values are noisy with some Gaussian noise with a standard deviation that is approximately known. How…
McLawrence
  • 263
  • 1
  • 8
6
votes
1 answer

Why noisy data will benefit Bayesian?

Recently I am reading a paper in 2001, Michael D. Ernst, Jake Cockrell, William G. Griswold, David Notkin Dynamically Discovering Likely Program Invariants to Support Program Evolution TSE 2001, in this paper, it says, Learning approach such as…
Cherry Wu
  • 289
  • 2
  • 11
6
votes
2 answers

Fit exponential distribution with noise

I'm trying to fit an exponential with noise (which in this case is a constant $c$) like this one $$ y(x) = \alpha e^{- \alpha x} + c \text{ ,}$$ having $(x_i, y_i)$ values (So $\alpha$ and $c$ are unknown and are the ones that I want to determine).…
Domenico
  • 101
  • 3
5
votes
2 answers

Noise in regression problems and ways to reduce it

In the theory of bias-variance decomposition for regression problems (this page is a very nice reference on this theory) the noise is defined as $$\mathrm{Noise} = \mathrm{E}_{X,Y}[(Y - \mathrm{E}[Y|X])^2],$$ where $(X,Y)$ is a pair of random…
Rodvi
  • 749
  • 1
  • 7
  • 17
5
votes
1 answer

Are colored noises correlated / uncorrelated?

Let, $x$ be a random variable (r.v) that is white Gaussian, has a flat power spectrum. $y$ can be any colored noise. I think another term for uncorrelated is i.i.d (identically and independently distributed). Colored noises such as pink, brown, and…
Sm1
  • 221
  • 1
  • 7
1
2 3
…
15 16