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Questions tagged [uncertainty]

A broad concept concerning lack of knowledge, especially the absence or imprecision of quantitative information about a process or population of interest.

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Neural networks with uncertainties in training data

I have used Flax to train a neural network to fit a model to some data. All of the data points have a known uncertainty, as in each row has both a value and an uncertainty. (To be more explicit: the ...
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Propagation of uncertainties for high signal-to-noise ratio measurements

I am writing mass spectrometry data reduction software which calculates 4He volumes, and I have some questions about the propagation of uncertainties. The system in question measures helium volumes by ...
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Getting accurate Uncertainty from MFVI?

I wanted to know if there has been any research on methods to improve the accuracy of Mean-Field Variantional Inference (which doesn't discard the mean-field approximation). Apparently it is known to ...
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Errors on cubic fits are relative to the order of magnitude of the y-data, not fit quality

I am writing some mass spec data reduction software (specifically, residual gas analysis mass spectrometry). I recently implemented a cubic fitting algorithm as an optional feature, however, some of ...
ohshitgorillas's user avatar
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How to determin the theoretical prediction limit for a complex process?

how can we find out what is the theoretical prediction limit for a complex process? For example, for a coin toss (on average) the prediction limit is 50%, that is we cannot predict better than this ...
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How can uncertainties in a generative model be propagated to an overall log-likelihood?

I am trying to use a Bayesian approach to carry out model selection and estimate the posterior distributions for parameters in a peak fitting scenario (quasi elastic neutron scattering). The ...
Andrew Nelson's user avatar
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How to aggregate the uncertainty around many predictions?

I have predicted trends for hundreds of time series. Each trend prediction comes with its own upper and lower bound at each time step. I would like to aggregate these trends and report them. Taking ...
Ress's user avatar
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Estimating variance from several samples

If several samples are taken from a distribution, say Gaussian, each sample having size n1,n2,n3,... and the SD of the underlying distribution is estimated from each of the samples, how can those ...
Maciej Tomczak's user avatar
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Distribution of tuples in chaotic sequences

I study infinite aperiodic sequences like Thue-Morse. Simple substitution rules allow you to get even more complex. I'm interested in the distribution of tuples in such sequences. For example, in Thue-...
lesobrod's user avatar
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Combining Variation and Uncertainty from Replicate measurements

I have 3 measurements from 3 independent experiments {m_1, m_2, m_3}. I have another 3 measurements that are used to scale the m measurements {n_1, n_2, n_3} from the same experiment (different from m)...
mAthletic's user avatar
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Quantifying prediction uncertainty using deep ensembles: How to combine Laplace distributions?

For a regression problem, I want to train an ensemble of deep neural networks to predict the labeled output as well as the uncertainty, similar to the approach presented in the paper Simple and ...
qubit's user avatar
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Parameter uncertainty in curve fitting

My real problem has a much more complexity and a different function than following. However, for the sake of simplicity assume I have a data that can be described as a one dimensional Gaussian ...
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Mean difference Confidence interval Repeated measures anova

I´m doing 4 repeated measures (T1, T2, T3 and T4) with each subject in the same conditions to calculate repeatability. I have 10 subjects. Could be correct to report posthoc mean differences 95% ...
mdscience's user avatar
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Error propagation: How to sum errors over 2D grid?

I have a dataset with worldwide mass change data and their uncertainty from glaciers. Both have dimensions 720,360,45 with the first two dimensions 'i,j' (lat,lon) coordinates and the third dimension '...
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Calculating the uncertainty of a very complicated variable

You have taken $N \gg 1$ measurements of a group of variables $V$. You want to estimate the value of a quantity $\mu$ that can be estimated from these variables. Fortunately you have a formula $\mu(V)$...
Bml's user avatar
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Determination of the uncertainty of the cosine of the half angle of a measured angle

I am trying to determine the uncertainty of the cosine of the angle, $\beta$, when the angle that is measured is $2\beta$. If the uncertainty in the measurement of $2\beta$ is 1 degree, then is the ...
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Binomial Test for data with normally distributed messurement error

I have a series of measurements and I want to perform a binomial test to see if the chance of exceeding some value $a$ is less or equal to some $p_0$. The measurement has some error which is normally ...
Adrian 's user avatar
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Standard error calculation for difference in means

In the case of two independent samples, the formula for standard error of the difference in means is given by : $$\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}$$ Even though we are talking about a ...
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Confidence and prediciton intervals for power law fit

I would like to determine confidence intervals and prediction intervals for a noisy dataset that follows a power law distribution. I have a dataset that (to my eye) follows power law behavior in the ...
Robert Zinke's user avatar
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Doesn't aggregating time series sometimes throw away quantifiable uncertainty?

Introduction From time-to-time I hear a claim that it is better to forecast on aggregated data because it is more "stable" or has less uncertainty. Here is an example, although I know I have ...
Galen's user avatar
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Unifying predictions from the same model but with differing assumptions

I'm working with the same dataset and I'm exploring several approaches to modelize it. Each model applies the same model but operates under different assumptions, such as: Stationarity vs. Non-...
Anewone's user avatar
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Detecting a single change point within an interval with a certain probability

I have a dataset $D$ of binary values, with length $|D|$. There is some unknown $d \in \left[1,2,\ldots,|D|\right]$ (usually, $d$ will be somewhere in the middle) such that, for any $i<d$, $D_i$ ...
Germ's user avatar
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How can I combine fully measured with partially measured outcomes in an IPD (Network) Meta-Analysis?

I was asked to help in an Individual-Patient Data (IPD) Network Meta-Analysis (NMA). Outcomes are supposed to measure combined scores of condition 1 and condition 2 (thus: $Score=Score_1 + Score_2$), ...
Federico Tedeschi's user avatar
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Binomial Test with uncertin data

I got observational data whether some elements of a population have certain characteristic or not. I want to see if the probability for carrying that characteristic is less than some threshold. This ...
Adrian 's user avatar
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1 answer
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Calculate mean and standard deviation of the ratio of two dependent variables

I have an instrument of which I would like to understand the uncertainty on the measurements taken, so that every time that I perform a single measurement, I can apply the error obtained and therefore ...
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Looking to extract patterns from sequences of codes

I have the following problem: I have a registration of people who enter a building, I have the name, entry date and end date. I also have the times at which events occur inside the building. I want to ...
slow_learner's user avatar
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Uncertainty in values predicted using a linear regression

I am quite new to statistical analysis, so this question might seem a bit obvious. My problem is the following. I have performed a simple linear regression between two sets of values without ...
Marc's user avatar
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Derivations of loss functions for learning loss attenuation in Bayesian DL

I'm fairly new to Bayesian deep learning, so sorry if this is a silly question. I'm trying to implement the work in this paper: What Uncertainties Do We Need in Bayesian Deep Learning for Computer ...
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Fitting uncertainty vs. bootstrap uncertainty

I'm currently working with some power law data of the form: $Y_i = \beta \times X_i ^{-\gamma} $ Where $Y_i$ are my measurements at point $X_i$. The uncertainty on $X_i$ is vanishingly small and can ...
AnImageAnalyst's user avatar
3 votes
2 answers
64 views

How can I assess case-level uncertainty of classification using logistic regression?

I'm hoping to fit a binary logistic regression to be used to predict the binary outcome for new cases/observations. I'm wondering if there is any way to gauge uncertainty of a prediction for ...
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How can Bootstrapping explain the uncertainty of a statistic?

I have been reading about bootstrapping, and sampling distributions, and find it odd that people use these techniques to describe uncertainty. As I understand it, the sampling distribution shows ...
Connor's user avatar
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How does the training set size affect the uncertainty (variance) of performance estimation?

I am reading this paper which discusses the factors that affect the uncertainty (variance) in the performance estimation of a learner. The authors say (p. 2, "The monotonicity of the learning ...
ado sar's user avatar
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6 votes
1 answer
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How can I quantify uncertainty for a least squares estimator in a multivariate linear regression with covariance structure?

Suppose that we have $$\mathbf{y}\sim\text{N}(\mathbf{X}\boldsymbol{\beta},\sigma^2\mathbf{M}\mathbf{M}'),$$ and let $\boldsymbol{\hat{\beta}}$ be the least squares estimator for $\boldsymbol{\beta}$. ...
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Coefficient covariance matrix of inverse probability weighted regression

I am interested in computing an estimate $\hat\Sigma_\hat\beta$ of the asymptotic covariance matrix of the parameter estimates $\hat\beta$ in a regression of $Y$ on $\{X, Z\}$, weighted by weighs $\...
Noah's user avatar
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MCMC uncertainties vanish

I'm trying to fit a signal modelled by a Lorentzian ($f(x) = \frac{\text{Amplitude}}{1 + 4((x-\text{center})/\text{width}^2)}$), thus I have 3 parameters : Amplitude, Central frequency and width. I ...
ttb's user avatar
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How to calculate the uncertainty of fitting parameter in a nonlinear model

I have a cost function which is: (F(X,B)-Y)*(F(X,B)-Y) F is my model, B are my fitting ...
MOON's user avatar
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How put uncertainty shading on a linear fit [duplicate]

I have a scatter plot and only my y values have uncertainty. I obtained a linear fit to my data (using curve_fit from ...
user1551817's user avatar
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Dataset to apply Sobol Indices

I am working on a project based on Sobol indices. I am looking for a dataset to apply Sobol indices. I need to show a basic demonstration of this method. Specifically, I want to identify which inputs ...
Dihan's user avatar
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How do i apply conformal prediction to achieve voxel-wise uncertainty quantification in a 3D binary segmentation problem?

Context: Typically in an image segmentation problem we go from the model's output logits to sigmoids to discrete labels (0 or 1 in this case). Akin to a binary classification problem, we can set up a ...
Amir Vahdani's user avatar
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1 answer
42 views

Optimization landscape and relationship to parameter uncertainties

In linear/nonlinear regression we try to find the minimum for the sums of squares as a function of the model parameters. For a one dimensional system this is just a simple 2D curve. Parameter on the x ...
rhody's user avatar
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Propagating uncertainty in a Markov chain with absorbing states

Consider a Markov model with two absorbing states $a$ and $e$ and three transient states, with an associated transition probability matrix like so: $$\begin{pmatrix} 1 & 0 & 0 & 0 & 0 \...
DRG's user avatar
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1 vote
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Contour plots limitations for stats models

My team prefers to present the models by using contour plots. Although I understand that they are looking nice I don't like them because they don't present the model's uncertainty but only the mean. ...
Lefty's user avatar
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0 answers
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Distance between two distributions with uncertainty / measurement error

I have two empirical distributions $X$ and $Y$, both with the same number of samples (a few thousand). $X$ are true values, they are accurate (i.e. no uncertainty). Values of $Y$, on the other hand, ...
qalis's user avatar
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Estimating variance based on parameters

I have a list of measurements $y_1,\ y_2,\ ...,\ y_n$ of quantity $Y$ and a list of parameters associated with each measurement $(A,\ B,\ ...)_j,\ j=1...n$. The distribution of $Y$ is symmetric, but ...
beregdsk's user avatar
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How to test statistical improvement under model prediction uncertainty?

I have a regression model, predicting a popularity of a text. I have its performance metrics on test set, e.g. RMSE and MAE. This gives me an uncertainty estimate about its predictions. Now I want to ...
qalis's user avatar
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1 vote
2 answers
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Definition of Uncertainity

I have some confusion regarding Measurement Uncertainty. In some books/articles it is defined wrt true value as "Uncertainty in the average of measurements is the range in which true value is ...
Govind Prajapat's user avatar
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MC Dropout without weight decay -- how is the model precision calculated?

In the original [MC Dropout paper][1], the variance is calculated as the sum of two contributions, an sample variance (over the multiple forward passes), var$(y)$, plus a term quantifying the inverse ...
till-m's user avatar
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1 vote
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Assessing uncertainty calibration in regression using the CDF

I have a labelled data set with $n$ data points $(x_i, y_i)$ with $x_i \in \mathbb{R}^k$ and $y_i \in \mathbb{R}$ and I trained a model $f: \mathbb{R}^k \to \mathbb{R} \times \mathbb{R}^+$ on some of ...
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Uncertainty of a distribution of classification results

Let us assume that a classification DNN is trained and dropout or model ensembling has produced n samples of a classification distribution. The two methods which I have seen to quantify uncertainty in ...
mkohler's user avatar
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Conformal prediction nonconformity measure

I want to implement offline inductive conformal prediction for a bianry classificstion task but I have an issue with finding an appropriate nonconformity measure. Shafer proposes the Nearest neighbor ...
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