Tagged Questions

A likelihood function gives the probability of observing the given data as a function of a parameter $\theta$.

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Learner in R to predict data from a complex function: if a>b then a*b else a+b

What sort of general purpose learner could learn the data generated by the following function: if a>b then a*b else a+b or something of that sort of complexity. Ideally something general enough to be ...
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Clear explanation of pseudo likehood

In generalized linear mixed model (glimmix) parameters are estimated using pseudo likelihood. I was trying to understand how this type of likelihood calculated. Thanks !!!
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Help in finding the pdf for Gaussian distribtuion of time series model

PROBLEM STATEMENT: The original data $y_t$ is a noisy version of a time series obtained from an autoregressive process excited by a deterministic non-linear signal $x_t$. The error terms $u_t$ is : ...
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Beginner learning resources : Pdf and likelihood function for non-Gaussian time series model

I am struggling with exercise problems related to blind system identification where the knowledge about the source input is assumed to be known using maximum likelihood estimation of univariate time ...
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jump in the sign of loglikelihood

I am trying to find the maximum of a loglikelihood function in a two dimensional parameter space__ e.g. X,Y positions are the free parameters__ by making grids in the parameter space and compute the ...
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Compare Two Logistic Regression Models

I have worked out two models to fit the data (blue) - the first (in green) is the baseline model with the intercept only. The second (red) is the model with the intercept and 2 parameters. Obviously, ...
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What would be the likelihood function of a pdf, $p(n)=1-|n|$ for $|n|<1$?

This might seem like a basic question to some but I am utterly confused by the fact that the given pdfs are not Gaussian or any other distribution commonly seen in examples. I have two hypotheses ...
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Hessian of Laplace distribution

The density of the Laplace distribution is given by: $$f(x;\mu,\sigma)=\frac{1}{2\sigma}\exp\left(-\frac{\vert x- \mu\vert}{\sigma}\right).$$ It is easy to see that this function is not ...
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Taylor's expansion on log likelihood

As far as I know, Taylors expansion works for fixed functions. I was wondering why it is justified to use it on the log likelihood. Even if we consider it as a function of only $\theta$, doesn't it ...
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Comparing OLS and ML through log likelihood value

The log-like likelihood values that are computed when I do a regression (by for instance eviews), are they comparable for different estimation techniques, specifically OLS and Maximum Likelihood? My ...
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Pseudo-likelihood and RBMs

I need to train a restricted Boltzmann machine to model the joint probability of categorical variables. For this I adapted a Bernoulli RBM to have groups of softmax units in the visible layer. The ...
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GARCH(1,1) Implementation question

Could someone shed some light into the implementation of the GARCH(1,1) model contained in page6 of the following document? ...
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Why is this likelihood function equal to the noise PDF?

My professor has this slide up here: Here, $y$ is an observed signal. $H$ is a deterministic transformation, which is assumed known. $f$ is the original signal (which we dont know), and $w$ is ...
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Can you calculate a AIC value using the non-linear maximization (nlm) minimum value in R?

So the formula for AIC is: AIC = 2k - 2ln(L) L is the maximized value of the likelihood function. I'm modeling oxygen data in R using Non-Linear Minimization (nlm) of a maximum likelihood estimation ...
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What is the likelihood of an observed standard deviation given a known normal distribution?

Given a sample X of size n from a normal distribution $N(\mu,\sigma)$ one can estimate $\sigma$ by $\hat{\sigma}$ from X. Then we know that: ...
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Likelihood Function for Complicated Transformations

Suppose that data X have a Normal distribution with some mean $\mu$ and some variance $\sigma^2$. However, you don't get to see X. Instead, you see $Y = g(X)$ where $g$ is a known function. Assume ...
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why minimize loss function instead of maximizing reward function?

Why is the "de-facto" in statistics to minimize the sum of squared errors cost function instead of maximizing some reward function like the likelihood function?
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Logistic mixed model

In the logistic mixed model ${\rm logit}(P(Y_i=1))= α + βX_i + u_i + ε_i , i=1,...,m$, when we know $u_i\sim \mathcal N(0,σu^2)$, and $ε_i\sim\mathcal N(0,σi^2)$, and if we know $σi^2$ in each area ...
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Unique or multiple maxima of log-likelihood function?

How can I find out if the log-likelihood function has only one global maximum or if it has multiple local maxima?
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how to find out the likelihood of a model given data

If i have a non-stochastic model that predicts the following dataset: [.2, .2] and the actual dataset found empirically (averaged over participants) is [.3, .3] How would I determine the ...
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Model selection for nonlinear regression of a Gaussian CDF mixture distribution

I have a number of distributions which I want to fit to a CDF that is comprised of one or more Gaussian CDFs. I was able to use weighted least squares regression to find the best fit parameters for ...
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Solving a difficult equation for a variable?

I'm trying to obtain the maximum likelihood estimate of the parameters for a model I'm building. I have constants $\sigma$, $\mu$, and $q_0$; a boolean matrix $\alpha$; and vectors $A, \beta, r, d,$ ...
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When is Likelihood Function Positive Semidefinite

This may be a very misinformed question, but I cant figure out why its not true. Here goes: According to Wikipedia and this post, the hessian of a likelihood function equals the information matrix, ...
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Likelihood score function 101

I have some trouble with score functions in likelihood calculation. I'm not good at statistics or probability, so I'm still confused on formalism and mathematical-probabilistic language. Some ...
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LogLikelihood Parameter Estimation for Linear Gaussian Kalman Filter

I have written some code that can do Kalman filtering (using a number of different Kalman-type filters [Information Filter et al.]) for Linear Gaussian State Space Analysis for an n-dimensional state ...
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Normalization of circularly-symmetric complex Gaussian distribution

I have a hard time describing my problem, but I'll try my best. It's all about the well-known zero-mean, circularly-symmetric, multivariate complex Gaussian distribution ...
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Likelihood principle: difference between weak and strong version

Does anyone understand the difference between weak likelihood principle and strong likelihood principle?
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Likelihood of a set of ranks

I'm sorry if I cannot formulate the problem precisely. Let us consider an ordered set $Q$ with $n_q$ elements, and a number of its (possibly overlapping) subsets $B_1, \dots, B_k$. For a given $i$, ...