Questions tagged [parameter-estimation]

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14 views

Why Maximum Likelihood Estimation for normal distribution?

Since we can compute the mean and the standard deviation of a set of random variables, why do we use Maximum Likelihood Estimation to estimate these parameters?
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29 views

Optimizing parameters for an image-generation algorithm

I have a program that takes an image and a list of parameters and generates a new image. I would like to automate the selection of parameters to produce the 'best' image. 'Best' in this context doesn'...
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1answer
680 views

Why Huber loss has its form?

Huber loss formula is $\hspace{3.0cm} L_\delta(a) = \begin{cases} \frac{1}{2} a^2 && |a| \leq \delta \\ \delta (|a| - \frac{1}{2} \delta) && |a| > \delta\end{cases}$ where $a = y - ...
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48 views

Estimation of hidden Markov Model from multiple time series

I've been using the depmixS4 package in R to estimate a single hidden Markov Model from 30 different time series (i.e., 30 different people). Initially, I took the clumsy approach of fitting a model ...
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19 views

Prove Zero Mean hypothesis

I have a stochastic process for which I can compute a final outcome that is real valued. I know that the process has a relatively high variance and is costly to obtain more data points. (which I know ...
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13 views

Mapping 4 Dimensional array to predicted output text

Iv been studying machine learning but im struggling with some concepts and cant seem to find particular answers to the question of how theoretical data is mapped into non classification categories. ...
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1answer
64 views

Paramaeter estimation in noisy conditions with Machine Learning, possible?

Let's take two constants, $\alpha$ and $\beta$, both are given by two functions $f_1(\vec{\theta})$ and $f_2(\vec\theta$) (the model). These functions are known: we have an analytical closed ...
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1answer
83 views

Can Expectation Maximization estimate truth and confusion matrix from multiple noisy sources?

Suppose we have $m$ sources, each of which noisily observe the same set of $n$ independent events from the outcome set $\{A,B,C\}$. Each source has a confusion matrix, for example for source $i$: $$...
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2answers
105 views

Maximum likelihood estimation vs calculating distribution parameters “manually”

I'm sorry for asking probably elementary question, but I cannot understand how estimating probability distribution parameters using maximum likelihood estimation method differs from calculating these ...
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2answers
89 views

Should I fit my parameters with brute force

I am running analysis on data for this type of sensor my company makes. I want to quantify the health of the sensor based on three features using the following formula: sensor health index = feature1 ...
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14 views

Number of events estimation

I have three different histograms (Impact parameter distributions) corresponding to three groups of the same particle with different properties. However, the three distribution have more or less the ...
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2answers
11k views

How to calculate ideal Decision Tree depth without overfitting?

What would be a good way to go around finding the best depth for a DecisionTree (in SKLearn)? How can I tell if I've gone too deep and am overfitting? I know I can find the best parameters with f.e. ...
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2answers
3k views

Parameterization regression of rotation angle

Let's say I have a top-down picture of an arrow, and I want to predict the angle this arrow makes. This would be between $0$ and $360$ degrees, or between $0$ and $2\pi$. The problem is that this ...
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1answer
73 views

What is “oracle” in statistics?

When I read several statistical papers, they mention "oracle" property or "oracle" estimator. What do they mean by "oracle"? I understand this oracle is not a company name, but have no idea what this ...
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850 views

How to tune weights in Voting Classifier (Sklearn)

I am trying to do the following: ...
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1answer
906 views

Parameter estimation for a model with multiple input parameters

So I've this model that simulates an ecosystem and outputs its attributes, like its chemistry, temperature etc. There are lots of input parameters to the model. My job is to write a program to figure ...
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972 views

Lasso implementation in Python

I am working on this course on Machine Learning 2012 from UBC (CPSC 340). I am stuck on a Homework code problem which shows the following RuntimeError in ...
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2answers
711 views

How does one fine-tune parameters and weights at the same time?

I have been having my hands full with training a model to classify web pages. This is the first time ever that I am doing this, so I know very little about ML. I'm here to learn. ...
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1answer
67 views

Finding parameters of image filter using classified pairs

I want to solve the problem of finding a parameter vector for an image filter (let us assume we know nothing about how the filter works, but we can feed it an input image and a set of parameters to ...
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3answers
9k views

CNN memory consumption

I'd like to be able to estimate whether a proposed model is small enough to be trained on a GPU with a given amount of memory If I have a simple CNN architecture like this: ...
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200 views

How do I choose the optimal parameters for reliefF

For feature selection I use reliefF provided by matlab. The reliefF function offers a parameter k to influence its ouput, additionaly for my specific task I can also vary a window length l on which ...
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1answer
758 views

Variance in cross validation score / model selection

Between cross-validation runs of a xgboost classification model, I gather different validation scores. This is normal, the Train/validation split and model state are different each time. ...
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2answers
1k views

What is 'parameter convergence'?

I'm trying to teach myself data science, with my particular interest being decision trees. A few steps in, I've come across a term, 'parameter convergence' that I can't find a definition for (because, ...
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1answer
164 views

Optimal parameter estimation for a classifier with multiple parameters

The image on the left shows a standard ROC curve formed by sweeping a single threshold and recording the corresponding True Positive Rate (TPR) and False Positive Rate (FPR). The image on the right ...
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2answers
1k views

generalized likelihood ratio test (GLRT)

I am having trouble in understanding the generalized likelihood ratio test (GLRT). Can anyone explain what it is to me, or point me toward an easy-to-understand reference? Is it a supervised or ...
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29 views

With EM algorithm, can you infer the location and variance of each “peak” in a pdf? Gaussian Mixture Models?

When I plot my data into bins, there is a frequency of data points per bin, which I can plot with a histogram. Based on this probability density function, I would like to find the maximum likelihood ...
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1answer
89 views

EM parameter estimation for conditional Gaussians

Let $$X_1\sim N(\mu_{X_1},\sigma_{X_2}^2)$$ $$X_2\sim N(\mu_{X_2}, \sigma_{X_2}^2)$$ where $\mu_{X_2}=c+aX_1$. Also, I have data $D$ (with missing values on $X_1,X_2$). How can I update/estimate the ...
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2answers
559 views

Gibbs sampling in R

I have the following model: $y_{it}=\alpha + x'_{it}\beta_{i} + \epsilon_{it}, \text{ } i=1,2,...,N, \text{ } t=1,2,...,T$ (1) $\beta_{i}= z'_{i}\gamma+\eta_{i}$ (2) with $\epsilon_{it} \sim N(0,\...
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1answer
154 views

Artificial Neural Networks and Efficient Parameter Optimization

I have a bunch of test measurements data and a semi-empirical model that has 18 parameters which I have to find so that the model fits my data well. So far I've managed to find and optimise the ...
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1answer
22 views

How to Ascertain Sine Wave and Harmonics

I have some data that I've noticed conforms to a sine wave. Details of the data is unknown. My task is to approximate it as closely as possible. From some experimentation in Excel, I noticed the data ...
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4answers
3k views

Which one first: algorithm benchmarking, feature selection, parameter tuning?

When trying to do e.g. a classification, my approach currently is to try out various algorithm first and benchmark them perform feature selection on the best algorithm from 1 above tune the ...
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1answer
12k views

Knn distance plot for determining eps of DBSCAN

I would like to use the knn distance plot to be able to figure out which eps value should I choose for the DBSCAN algorithm. Based on this page: The idea is to calculate, the average of the ...
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0answers
31 views

How to estimate the lambda e to reweighing translation probabilities of entries in a dictionary

In this paper, Brown et al. proposed the usage of dictionary entries in addition to the machine translation probabilities that we're used to in the Math of Statistical Machine Translation. He ...