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

learn more… | top users | synonyms

0
votes
0answers
22 views

Conceptual links and differences between generalized and latent models

Please bear with me, as I'm trying to better understand these aspects. My understanding is that (finite) mixture models (MM) are characterized by a presence of a number of sub-populations in a ...
1
vote
0answers
20 views

Mixture distribution fitting for latent variable analysis

Are there any analytic approaches to using mixture distribution fitting for latent variable analysis? I'm specifically interested in existing approaches to determining whether mixture components ...
2
votes
1answer
52 views

What is the role/purpose of hidden variables in graphical models?

Is there a formal treatment of the role/power of latent/hidden variables in graphical models and other machine learning models (e.g., structural equation models)? For example, the Restricted Boltzman ...
0
votes
1answer
37 views

identifying latent variables in this model

I have been trying to understand EM and I am having a hard time understanding what a latent variable is. In particular, I am having issues in identifying whether in a particular model that I am using, ...
3
votes
1answer
97 views

How to choose the optimal number of latent factors in non-negative matrix factorization?

Given a matrix $V^{m \times n}$, Non-negative Matrix Factorization (NMF) finds two non-negative matrices $W^{m \times k}$ and $H^{k \times n}$ (i.e. with all elements $\ge 0$) to represent the ...
0
votes
0answers
12 views

How to model occurrence counts in groups with latent membership?

I have a dataset that describes the daily history of occurrences of a certain phenomenon P among a certain population. (These are subdivided into certain classes or forms that the phenomenon can take: ...
0
votes
0answers
37 views

How to use factors (from CFA) as independent variable in Regression Analysis

I calculated 4 factors as latent constructs in a Confirmatory Factor Analysis (I use AMOS). Now I am wondering if it is possible to extract some kind of a factor score like I know it from Exploratory ...
0
votes
2answers
79 views

Binary version of Probabilistic Matrix Factorization in pymc?

I'm a newby in statistics, this is my first post, sorry for any possible mistake. There is a good Bayesian Probabilistic Matrix Factorization model introduced in: Bayesian Probabilistic Matrix ...
0
votes
0answers
12 views

Mixture model for dependent observations with additive group-level confounders

I'm looking for a special type of mixture model (described below) and I'm hoping to get some hints with regards to relevant literature to look at or names to be searching for. On the general level, ...
0
votes
0answers
53 views

Estimation parameters for latent (unobserved) variable

Here is my problem: I have 3 variables $X,Y,Z$ : $X$ is the number of clicks we observed on an web advertisement; $Y$ is the number of time a customer do a sign-up on the website after clicking ...
0
votes
1answer
36 views

Partial Least Squares structural equation modeling

Im calculating a Structural Equation model with Partial Least Squares (with R). Lets say a simple example: two Response values (R1, R2) are combined to a latent variable RespLV = weight1*R1 + ...
0
votes
0answers
13 views

How to compute a latent variable (as a composite index)

Can I calculate a latent variable (construct) after the formula: Sum of (summated scale for factor i * variance explained by factor i), i taking values from 1 to n (n being the number of factors)? Or ...
0
votes
0answers
23 views

Summated scales for factors (latent variables)

For summated scales: can I use all the variables that load on a factor or is it necessary to use only the variables with high loading (over 0.70, for example)?
0
votes
0answers
6 views

Summated scales (after factor analysis)

Is it okay to calculate summated scales if I am undertaking an exploratory study and some of the factors that result are somewhat different from what I have found in the literature? (I have read that ...
2
votes
0answers
41 views

latent variables versus model parameters

I am quite confused with the distinction between a latent variable and model parameters. So say I have two observed variables $x$ and $y$ and they have some unknown relationship between them i.e. $y ...
0
votes
0answers
19 views

Splines, latent variables and model identification

For $J$ units of observations my basic model contains latent variables $\theta_j, j = 1, ..., J$ identified by the marginal assumption $\theta \sim N(0,1)$. The latent variables are connected to a ...
1
vote
0answers
13 views

Is there an extension of multinomial process models that can model several dependent variables?

In the setting of psychological experiments, where a categorical response has to be given, the same response can, in theory, be generated by different latent processes. For example, in an experiment ...
0
votes
1answer
57 views

Bayes' theorem in 1-d EM algorithm

I'm watching a video on the EM algorithm, When we use Bayes' Theorem to calculate $b_i$, how do I find $P(b)$ and $P(a)$ initially? It says we can estimate the priors $P(b)$ and $P(a)$ but that's ...
4
votes
2answers
106 views

EM algorithm Practice Problem

This is a practice problem for a midterm exam. The problem is an EM algorithm example. I am having trouble with part (f). I list parts (a)-(e) for completion and in case I made a mistake earlier. Let ...
1
vote
2answers
66 views

Can I test hypothesis based on estimated parameters?

I estimated the mean and variance of two latent variables through two groups of data. I can't use the data to do hypothesis testing, because I am interested in the latent variable. Is there a way to ...
0
votes
0answers
93 views

The difference between SVD and SVD++

What if any is the connection between SVD (the one you learn about in your linear algebra course) and SVD++ (the one from the Netflix prize)? I know they both want to find latent factor spaces. But ...
0
votes
2answers
95 views

Compute the user and item features in SVD++

I have a sparse matrix. There is lots of missing data. Hence, I can't use SVD naively. I read Koren's SVD++ paper. I'm confused as to how the $q_i$ and $p_u$ vectors are determined. $q_i^Tp_u$ is ...
1
vote
0answers
42 views

Use of latent semantic analysis in topic modeling

I am learning Latent Semantic Analysis. After we have the SVD decomposition of term-document matrix, how to infer topics from that? For example, how to get the following result: Topic 1: apple, ...
0
votes
0answers
47 views

How to make the most of a Gaussian mixture assumption in a model?

I have a dataset with 100 columns and approximately 100000 lines. I have a variable to predict that is Y (0,1 so it's a classification problem). I have an other categorical variable with two values ...
1
vote
0answers
55 views

Log Transformation in Growth Mixture Model (Mplus user)

I am using parallel process growth mixture modeling to estimate bidirectional longitudinal dynamics between pain (0-100 theoretical range of values) and sleep measures (0-20 theoretical range of ...
4
votes
2answers
102 views

Are there any good papers comparing different philosophical views of cluster analysis?

Lots of people use cluster analysis. I've heard very few explicitly say why. I imagine this is because within a given field, most researchers seem to understand why clustering is used for the problems ...
2
votes
0answers
75 views

Variational Posterior Dirichlets in LDA

I am running the c code for LDA provided on David Blei's website. The code outputs several files. The output file final.gamma is supposed to include the "Variational Posterior Dirichlets". If I ...
0
votes
0answers
29 views

Convert latent proabilities to categorical realizations

Assume that each person can only have one of three eye colors: blue, brown or green. Furthermore assume that I have two vectors that describe for N individuals the (simulated) probability that the ...
0
votes
2answers
58 views

Can I turn a latent variable be treated as an observed variable?

I am a doctoral candidate and for my dissertation research I am using two standardized scales as independent variables. Because these have not been used with my particular population, I needed to do ...
0
votes
0answers
23 views

Categorical observed, Binomial latent

This might be a trivial problem but wondering if there's a known model formulation for this problem: Ordered Categorical observed variable with k levels Trying to estimate the parameter (theta) of a ...
2
votes
2answers
81 views

What happens when you initialize EM with a consistent estimate?

I have a certain family of models with latent random variables (not observed in the data), which I have a consistent estimator for. I now run EM on top of it, meaning, I get the consistent ...
3
votes
1answer
128 views

What are the main differences between classical and Gibbs sampling Latent Dirichlet Allocations?

In these weeks I have been studying the classical Latent Dirichlet Allocation (LDA) algorithm by David Blei and colleagues (2003), and the LDA variant based on Gibbs sampling introduced by Tom ...
0
votes
0answers
17 views

Heuristic to determine the number of latent classes [duplicate]

Do anyone know if in the field of topic modeling and latent class analysis, it exists some heuristic to determine a good number of latent classes?
1
vote
0answers
43 views

Map a normal distribution N(x,s) to an ordinal response variable

I am working on a simulation. I am going to extract a series of normally distributed values from a distribution $N(x,s)$ whose mean $x$ and variance $s$ is known. I want to pretend that such ...
1
vote
1answer
79 views

Latent Dirichlet Allocation - understanding the posterior

I have a problem understanding the posterior for computing LDA, stated in page 7 of Blei (2007). From my point of view, it's not exactly consistent with Bayes' theorem, as described here. Could anyone ...
2
votes
0answers
78 views

Model validation in Bayesian statistics from a model with latent variables

I am working with some two-regime autoregressive models first introduced by Hamilton in 1989. The specific models is of no great concern to my question, but some variables within my autoregressive ...
1
vote
0answers
27 views

Latent partitioning variable governing unobservable dependent variables — how to estimate?

I've got a situation where I'm willing to assume that $$ Q_t = Pe^{-k^f t} + (1-P)e^{-k^s t} $$ This is a double-exponential decay model; there is a fast pool and a slow pool. I don't know $P$, ...
1
vote
1answer
137 views

Problems in scale Bayesian network mode using R

The problem that we have is as follows. We have close to 60 discrete random variables each of which shall take on an average of 5 categorical values. We have developed a Bayesian network ...
1
vote
0answers
77 views

Gibbs sampling in document clustering

I'm trying to understand the idea behind the Topic Models in document clustering. In Latent Dirichlet Allocation, it is necessary to approximate the posterior distribution of topics over the document. ...
1
vote
0answers
48 views

Unexpected correlation, Method to account for the shared variance

Summary: While looking at the correlations I noticed a wired, relation. Variables: User comments - A; User community activity- B; Comments on user's wall - C (control) I expected that A and B will ...
-1
votes
2answers
169 views

Any good book for learning probability programming

Are there any good books for me to learn probability programming? For example, I am new to Latent Dirichlet allocation (LDA) and Gibbs sampling. I have read some books about the techniques, but it ...
0
votes
1answer
37 views

Likelihood of a document-term matrix

I'm trying to deeply understand the Probabilistic Latent Semantic Indexing (PLSI) algorithm. The generative model underlying the algorithm is the folowing: Select a document $d \in D$ with a ...
1
vote
0answers
52 views

Probabilistic latent semantic analysis - objective function

In pLSA we are trying to maximize a likelihood function. The likelihood function can be given as: $L = \Pi_i^N \Pi_j^M P(d_i,w_j)^{n(d_i,w_j)}$ Where, M is the number of words and N is the number of ...
1
vote
1answer
145 views

Confusion related to EM algorithm

I was reading this tutorial related to EM algorithm at http://aass.oru.se/~tdt/ml/extra-readings/EM_algorithm.pdf. As given in the tutorial we can see that at each E step we calculate the ...
2
votes
0answers
36 views

How to model the distribution over payouts from aggregate (sum) data?

The setting A user is presented with a number of options related to her search query. She chooses one of them. As a result, a payout is generated. The payout lies in the range [0.05, 0.50], but the ...
2
votes
1answer
66 views

Are latent variable models modelling causality?

Is the purpose of latent variable models to model causality, where the causes are not observable i.e. latent? Are latent variables modelling causes of the observable variables? Thanks and regards!
5
votes
2answers
726 views

Expectation maximization on Bayesian networks with latent variables

I am trying to determine parameters in a bayesian network with two latent variables (in blue). Every variable is discrete with 2-4 categories. The latent variables have 3 categories each. I am ...
2
votes
1answer
118 views

Why does latent variable modelling in regression tend to push R Squared up?

Hypothetically, say I have I have three manifest variables measuring anxiety and three manifest variables (items) measuring stress. Then I want to use both to predict scores on depression, which I'm ...
5
votes
1answer
141 views

IRT/Rasch modeling with very large N

I want to fit a 1-parameter IRT model on a questionaire with 15 questions and about six million people. Considering the large N, standard errors aren't essential. It looks like the IRT world is sort ...