Latent variables refer to variables that cannot be directly observed. These variable are defined in terms of observable variables.

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Text mining by using LDA [on hold]

I am struggling by using analysis by using lda in R . I have termdocumentmatrix already. Pleae tell me how to proceed. I have tried so many versions however could not get any benefits from these. ...
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40 views

“Unable to resolve the following parameters:” jags error for Latent Class Model

I'm looking to fit a Bayesian latent class model in JAGS, but am running into an issue, which I'm seeking help to resolve. The model I'm trying to fit is described below (model details can be found ...
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shall I expect the same number of clusters in cluster analysis as the same number of classes from LCA?

Both cluster analysis and latent class analysis are aiming to group the cases into groups. If I do cluster analysis and latent class analysis using the same variables, shall I expect the same number ...
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18 views
+50

why Matrix Factorization map the two spaces into same space?

It's a common sense that Matrix Factorization map the two space(e.g. user and item) into same factor space. But is there any more formal way to explain the fact they are the same ? Say, I have $$\...
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Given a topic distribution over words from LDA model how to calculate document distribution over topics for new document?

I'm using Spark 1.6.2 via the Python API. It seems that as of when this post is being written, the only data available from the LDA (latent Dirichlet allocation) model calculations is a topic ...
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16 views

Analyzing a Randomized Response Technique with Structural Equation Modeling (SEM) in R

I would like to analyze a Randomized Response variable as the final response variable in a Structural Equation Model (SEM), with R. However, I found no example ...
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Can I try several CFAs on the same data before choosing one for generating factor scores?

I'm interested in matching on latent constructs (see Raykov, 2012), and one way to do so is to match on factor scores generated by CFAs of the items (One could also match on the items themselves, but ...
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1answer
32 views

Learning just a decoder (autoencoder without encoder)

I am trying to do something quite unusual: learning a latent representation of some data just by optimizing a decoder. Basically, a probabilistic model of a neural network autoencoder without the ...
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13 views

Factor analysis without listwise deletion

Can factor analysis be done in a manner which affords missingness on some items? With what methods can a factor analysis be performed in which subjects who are missing on one or two items are ...
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56 views

Change Score Model in lavaan

UPDATE: I think I was over-complicating my problem and am struggling through a new approach as described here: paired t-test as a simple latent change score model I am still accepting the answer ...
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27 views

Jags Implementation of Multivariate Response Probit Model

I am trying to implement the latent variable interpretation of a probit model with vector response (described on wiki here), but am receiving an error. In this model, we have a matrix $X$, $n \times ...
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48 views

Logistic regression and latent data

Assume a simple logistic regression model: given binary data $y_1,\ldots,y_N$ where for each $1 \leq i \leq N$ the outcome of $y_i$ depends on one variable. The succes probability is $p_i = \mathbb{P}(...
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Gibbs Sampling for LDA example

Can someone provide an example of 1 (or more) iteration(s) of Gibbs sampling for LDA using real values? I have been searching for a while and I can't seem to find any good examples. Thank you.
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Appropriateness of Rasch for formative measurement models

I've spent several months thinking about the issue of whether or not it is appropriate to apply Rasch models to formative measurement models, and I'm looking to see whether anybody else has considered ...
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20 views

Is it possible to correct for bias in the tetrachoric correlations of rare events in small samples?

Background and Problem I am developing a meta-analytic epidemiological model to predict the prevalence of a series of related psychiatric disorders. As part of the modelling process, I need to ...
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21 views

How to update latent discrete variables in MCMC?

Most of the discussion on Bayesian model with latent variables that I've seen fall into two classes: continuous latent variable underlying the observed discrete outcome (e.g. probit model (Albert ...
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37 views

How to determine time complexity of EM algorithm of probabilistic PCA?

I was studying probabilistic PCA from Bishop's book. There an EM algorithm is provided to calculate principal subspace: Here $\mathbf M$ is $M\times M$ matrix, $\mathbf W$ is $D\times M$ matrix ...
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1answer
67 views

Understanding LDA inference

It is said that the key inferential problem that needs to be solved to use LDA (latent Dirichlet allocation) is that of computing the posterior distribution $p(\theta,z | w, \alpha ,\beta)$. I know ...
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2answers
52 views

Word-topic matrix in Latent Dirichlet Allocation

In the latent Dirichlet allocation model described in Wikipedia, is $\beta$ the word-topic matrix? I understand that $\beta$ is the topic-word matrix and that $\beta_{ij}$ contains the probability ...
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1answer
87 views

What is principal subspace in probabilistic PCA?

if $X$ is observed data matrix and $Y$ is latent variable then $$X=WY+\mu+\epsilon$$ Where $\mu$ is the mean of observed data, and $\epsilon$ is the Gaussian error/noise in data, and $W$ is called ...
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16 views

How to classify new observations to class with parameters from latent class models?

I am using LatentGold to estimate latent class models (LCM). I wanted to create an equation using my parameters to classify new observations to classes. To do this, LatentGold explains that one has ...
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1answer
104 views

Probabilistic models for partial least squares, reduced rank regression, and canonical correlation analysis?

This question results from the discussion following a previous question: What is the connection between partial least squares, reduced rank regression, and principal component regression? For ...
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1answer
100 views

Beginner references to understand probabilistic principal component analysis (PPCA)

I am totally new to machine learning. I started studying PCA from Jonathan Shlens, 2005, A Tutorial on Principal Component Analysis. The paper provides some concrete examples, and background ...
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25 views

How to estimate the parameters of the following log-likelihood function?

I would like to estimate the parameters based on the famous Merton model used probability of default modelling: Suppose firms' logarithmic returns are following the standard normal distribution and ...
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17 views

Defining a paradox in proxy variables

I'm looking for a formal name or write-up of this phenomenon: When you are using a proxy variable (A) to measure a true variable (B), taking actions based on the proxy variable can decrease the ...
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36 views

Mixture model: 3 binomial components (using WinBugs)

I've a BUGS question I've been unsuccessfully trying to define the likelihood for a mixture of 3 binomial distributions. I'd like to estimate the mixing proportions for a given dataset. Once this is ...
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Sampling a Latent Variable with Unspecified Distribution

This question is an extension of Product probability , although I will use a linear function here in hopes it is more tractable. Suppose you observe a sample of iid random variables $Z_1,...,Z_n$. ...
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34 views

A question regarding maximum likelihood conditional on observed and unobserved variables

Please consider the following likelihood function, $$ L(\theta)=\int_{\Re^u} f(y|x,u,\theta)h(u|\xi)\partial u, $$ where $y$ is a vector of observed dependent variables, $u$ is a vector of unobserved, ...
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57 views

When does the marginal MLE converge to the complete data MLE?

What I mean by the title is suppose we have a distribution $p(x,z\;|\;\theta)$, where the $x$ are observed and each $x_i$ depends on a hidden $z_i$. Then the marginal MLE is given by $$\max_\theta\...
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25 views

Does it make sense for pairwise correlation to be uniformly higher than an ability?

I am fitting a graded response model to analyze a 16 item, 5 ordered response category scale. It shows univariate properties in an EFA with one very strong principal component. The distribution of ...
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27 views

PCA (with phi/tetrachoric correlation matrix) or LTM to study the correlation of (true) dichotomous variables?

I am doing a study were I have to initially do an exploratory analysis by grouping diseases that coexist in my study population. For that I have x diseases/variables that are (true) dichotomous (...
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17 views

Intuition behind Latent SVM

The relevant paper (I think) is : Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32....
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Under the latent variable formulation, can we use the Tobit model for count or categorical dependent variables?

Suppose we are using the Latent Variable Formulation (i.e. not the Generalized Linear Modeling) for a multiple regression model: Can the Tobit model be used if the dependent variable is not ...
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57 views

Why does probabilistic PCA use Gaussian prior over latent variables?

I am currently reading papers about probabilistic PCA and I am wondering why is Gaussian prior (and not some other prior) chosen for the latent variables? Is it just because it's simple or is there ...
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Modeling target effect as latent variable - Why not possible?

I have a dataset with participants nested in groups of six, and each participant is rating the other participants in his group with respect to trustworthiness. The average rating of all other ...
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1answer
168 views

What's the difference between a MIMIC factor and a composite with indicators (SEM)?

In structural equation modeling with latent variables (SEM), a common model formulation is "Multiple Indicator, Multiple Cause" (MIMIC) where a latent variable is caused by some variables and ...
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1answer
53 views

What is the motivation for the entropy term in the proof of EM algorithm?

Reading through the proof that EM algorithm monotonically increases the log-likelihood (until it converges), I noticed that the most important ingredient of the proof is the introduction of an entropy ...
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25 views

SEM with zero-inflated outcome

I'm working on a project and my advisor wants me to do SEM in MPLUS because we can do latent modeling, but I have no clue where to start. I've only ever done regressions but I said I'd be up for ...
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28 views

Signal-to-noise ratio for probabilistic PCA

Consider the probabilistic PCA model where you have $n$ i.i.d centered obserbations $x_1,...,x_n\in \mathbb{R}^p$ drawn from $$\forall i\leq n, \; \; \; \; x_i = W y_i + \varepsilon_i,$$ where $W$ is ...
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1answer
29 views

Latent Dirichlet Allocation and text Pre-Processing

I think I understand the basic principles of LDA. However, browsing the githubs of people who applied this method, I noticed they pre-process the Corpus very specifically. For example, about the ...
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Constraining the mean latent slope in lavaan

In lavaan, I can constrain the intercepts of the latent slope and latent intercept, like so: ...
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114 views

Comparing AIC/BIC Between Continuous (CFA) and Categorical (LCA) Latent Models

Some colleagues and I have a set of variables that we would like to represent more parsimoniously/latently. Originally, my colleagues used an exploratory and confirmatory factor-analysis approach to ...
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1answer
105 views

Advantage of latent SVM for part-based object detection

In the famous paper Object Detection with Discriminatively Trained Part Based Models, the authors use a Latent SVM approach to learn the detector of each part, because the localization of the parts in ...
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1answer
97 views

Mathematics behind factor loading in Confirmatory factor analysis/ Structural Equation Modeling

I'm curious about how are the loading in a simple confirmatory factor analysis determined mathematically. Also, the intuition is also hazy to me as to how are the factor loads determined when a ...
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36 views

Fitting a logistic function to a latent growth curve model in lavaan

Assume I have the following latent growth curve model in lavaan: ...
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1answer
31 views

SEM/Bayesnets: does creating a latent variable always reduce the fit?

assume there are 3 variables (x1, x2, x3) that each correlate tightly with each other, and also correlate tightly with y. if we add a latent/hidden variable that merges information from x1, x2 and x3 ...
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47 views

Rotation in (Univariate) Partial Least Squares Regression

according to a not so recent paper (http://www.sciencedirect.com/science/article/pii/S0167947303003049), it is a good idea to Varimax-rotate the factors that have emerged by Partial Least Squares. ...
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Non-time-varying covariates in latent growth curve modeling?

I know that if I'm doing a latent growth curve model I can use time-varying covariates, i.e. a covariate that has one value for each time point. What if I have a variable that has one value for each ...
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48 views

Gradient of Expectation in latent variable model

I am reading a paper that talks about minimizing the variational lower bound $\mathcal{L}(\theta,\phi;x) = -KL(q_\phi(z|x) ||p_\theta(z)) + \mathbb{E}_{q_\phi(z|x)} log(p_\theta(x|z) $ wrt. to the ...
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1answer
39 views

What is the conceptual link between the chi-square distribution and indices of fit in structural equation modeling?

I understand that the fitting function in SEM is obtained from the difference between the observed and model-implied covariance matrices. I can't find a simple and intuitive explanation of -why- it is ...