Questions tagged [parameter-estimation]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1
vote
1answer
14 views

how to find the best parameters to solve a differential equation?

I have a differential equation: def func(Y, t, r, p, K, alpha): return r * (Y ** p) * (1 - (Y / K) ** alpha) and I want to find the best parameters that fit (r,...
0
votes
0answers
9 views

Estimate class proportions of a feature, central limit theorem

haven't been feeling smart lately and this is probably the most trivial question ever but I really need to know. I'm trying to point estimate some population parameters. I sampled from 1000 randomly ...
1
vote
0answers
13 views

Correcting high AR(1) coefficients in dynamic Gordon model

I have just finished my thesis on a heterogeneous dividend expectations model applied to the COVID-19 crisis! However after receiving some feedback there is one last issue I want to resolve. I'm using ...
1
vote
0answers
5 views

How should rolling window of parameter estimates look like?

I am using Orstein-Uhlenbeck model to model inflation: $dI_t=\theta(\mu-I_t)dt+\sigma dW_t$. I have plotted rolling window estimates of all the parameters. However, I do not understand what to ...
1
vote
1answer
42 views

Pearson correlation coefficient - is correlation estimator acceptable?

As far as I know when it comes to theory, we use Pearson correlation when we want to check linear correlation between two variables, which are both continuous or discrete. For a mixed case it's not so ...
0
votes
0answers
31 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?
0
votes
0answers
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'...
4
votes
1answer
1k 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 - ...
1
vote
0answers
18 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. ...
2
votes
1answer
77 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 ...
4
votes
1answer
92 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 $i$: $$...
0
votes
2answers
137 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 ...
2
votes
2answers
127 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 ...
1
vote
0answers
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 ...
1
vote
2answers
16k 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. ...
16
votes
2answers
4k 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 ...
1
vote
1answer
103 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 ...
2
votes
0answers
1k views

How to tune weights in Voting Classifier (Sklearn)

I am trying to do the following: ...
-1
votes
1answer
963 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 ...
1
vote
0answers
1k 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 ...
1
vote
2answers
767 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. ...
2
votes
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 ...
11
votes
3answers
12k 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: ...
1
vote
0answers
226 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 ...
1
vote
1answer
897 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. ...
2
votes
2answers
2k 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, ...
1
vote
1answer
168 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 ...
3
votes
2answers
2k 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 ...
1
vote
0answers
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 ...
1
vote
1answer
90 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 ...
5
votes
2answers
679 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,\...
2
votes
1answer
161 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 ...
0
votes
1answer
25 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 ...
11
votes
4answers
4k 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 ...
9
votes
1answer
15k 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 ...
2
votes
0answers
33 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 ...