Tagged Questions

Used for statistical models expressed via graphs, causal or not. ("graph" here as in graph theory). See https://en.wikipedia.org/wiki/Graphical_model

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what's the period for state in Markov chain?

I have two questions: 1.What's the period for state A or does A have a period? Is there a specified name for those states like state A who gives all to other state and won't be returned back? Are B ...
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Directed graph algorithm

Say I have the following graph: ...
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Looking for proof of conditional dependence, when the conditioning variables are linearly related

Suppose we have three random variables, $X$, $Y_1$, and $e$ (for error). Variable $e$ is independent of $X$ and $Y_1$, but $X$ and $Y_1$ are dependent. Further suppose we construct a new mixture ...
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Analysis to show averages for more each year of exposure to a treatment does not shift the averages toward an expected average

I apologize if there is appropriate terminology I should be using in my description that I do not know. Hopefully the way I've made my question generic for the sake of preserving the integrity of my ...
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Prediction of vertex scores in a bipartite graphs

I have a bipartite graph with two sets, A and M, of nodes. Every vertex in M has a score associated with it. I have two tasks: To every vertex a in A, I have to assign a score based on the scores of ...
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How does loop belief propagation differ from variational message passing?

I'm reading Yedidia et al.'s paper and Winn et al.'s paper. The two approaches (LBP and VMP) are pretty similar to each other: Eq (5,7) in Yedidia are similar to Eq (19,20) in Winn. They both update ...
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Can (loopy) belief propagation be used to learn from a data set?

I'm trying to expand my experience with restricted Boltzmann machines to a more general class of graphical models and currently learning about belief propagation using message passing algorithms. One ...
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Is linearity an issue for structural learners?

I have a matrix of z-scores. Let's say these z-scores are trustworthy and that the assumption that the data fits a normal distribution has been tested for our data. I have a large feature space (>...
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Dealing with auxiliary random variables for Mean-Field Variational Inference in Bayesian Poisson factorization

I am studying as a part of a class assignment a recent paper on Poisson factorization. Some points of the paper regarding the usage of some auxiliary variables are not clear to me. I would like to ...
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R - glasso very slow for high feature space

all, I'm doing a graphical lasso in order to approximate the inverse of the covariance matrix of a 1200 (p-features) by 100 or so (n observations) data matrix. Basically, I'm inverting a 1200 x 1200 ...
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Using Clustering Coefficient to Improve Naive Bayesian Classifier

I am new at statistics and ML. Due to my lack of theoretical background I was wandering if does it make sense to combine NBC and CC. I am participating to the kaggle competition https://www.kaggle.com/...
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Visualization for discrete integers on a non-fixed domain

So I am wondering if someone can give some input on how I might go creating some good visualization for this data set I have. Lets say that I am allocating buffers in some code that I have and that ...
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How to predict with the known information in an undirected graph

Protein-protein interaction networks are known. It is an undirected graph. Each row of the networks is like this (Protein 2 - Protein 6), and It represents the interaction between Protein 2 and ...
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What are the values of output g_i in Bengio's paper “Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks”

Figure 2 of Bengio's paper "Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks" http://www.iro.umontreal.ca/~lisa/pointeurs/jdm.pdf describes a neural network ...
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Markov Cluster Algorithm transition matrix

I am reading the notes on Markov Cluster Algorithm by Kathy Macropol (http://www.cs.ucsb.edu/~xyan/classes/CS595D-2009winter/MCL_Presentation2.pdf) On slide 14/46 the author talks about inflation and ...
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I am looking for pointers (papers, algorithms etc) for learning models for sequence tagging but which allow for additional structure. Consider Part of Speech Tagging, I could train a CRF which would ...
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Create a precision matrix and get desired covariance matrix

I am trying to build a Gaussian graphical model for a simulation. I want to achieve the following: Simulate an undirected graph structure (Markov network). Take nodes as variables and edges as ...
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Is it possible that maximal probabilities in hidden markov model are same for all sates?

I developed forward/backward algorithm for calculating probabilities for each state in hidden markov model and I got that maximal probabilities are the same in final probability matrix. This is data ...
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Does this graph show homoscedasticity or heteroscedasticity? Are the errors random?

I think this graph shows homoscedasticity. Is this true?
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Probabilistic models for sales forecasting on irregular intervals

I'm working on a very similar problem to the one presented here by @Ivan Dimitrov. The task is to predict how many products will be sold in a given time interval, knowning the past sales (which are ...
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Can two configurations (values) of sets of variables be independent, as opposed to two sets of variables?

I've been having a discussion about the validity of this idea, which seems to have originated from the definition of conditional independence found in the book "Probabilistic Graphical Models" by ...
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EdgeRank for eCommerce product feed

I am running my eCommerce store with around 1000 products with 10 categories. I want to show these products in feeds. But there are lots of products so its very complex to define priority list for ...
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Representing the joint distribution of a cyclic directed graph with an undirected model

Are there any directed cyclic graphs whose joint distributions cannot be represented by an undirected graph (assuming no limits on connectivity)?
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How to get a probability distribution by combining multiple individual distributions

I want to classify some data, but I can only observe some of that data at any one time. Unfortunately, there is no trivial way of combining multiple observations into one single form. For each of ...
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Modeling a Classification Problem with an Undirected Graphical Model

I have an undirected graphical model problem which I'm looking for some help on. So, the goal is to perform multivariate classification: based on a set of observations, I want to predict the correct ...
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How can I solve this graphical model?

I have a classification problem, with the following structure. There is a fully-connected graph, and each node needs be assigned a class label. Every pair of nodes in the graph has a probability ...
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How to do inference in (undirected) triangular graphical network?

I have dataset of sequences where every element of a sequence is a vector. The goal is to classify entire sequences into $K$ classes. The important part is that the sequence, not the elements, get the ...
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Counting non-reduntant parameters in probabilistic graphical models

Let's say we have the following problem from this book: Consider a very simple medical diagnosis setting, where we focus on two diseases — flu and hayfever; these are not mutually exclusive, as ...