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-1
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0answers
5 views

Using googleVis api in java [migrated]

I have used googleVis api in R for dynamic map.Now i am trying to make a small webapp in which servlet access this api and the result is displayed in jsp.But i am not able to access googleVis api from ...
0
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1answer
18 views

Message passing (belief propagation) in practice - observed variables

In a graphical model with variables with continuous distriubtions, and some observed variables, how can I compute the messages to be passed? I know the messages but I don't know how to implement it? ...
0
votes
1answer
17 views

Probabilistic Logical Graphical models like Markov Logic networks etc

I can't quite get a grasp of how and where these Probabilistic Logical Graphical Models (or PLGM or Statistical Relational Learning Models) score better than ordinary Probabilistic Graphical ...
1
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1answer
40 views

Does a Bayesian network include the CPTs?

I'm preparing slides for a lecture, and I require some guidance. I'm only talking about discrete variables. How would you formally define the concepts surrounding Bayesian networks? A Bayesian ...
0
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0answers
18 views

Best way to graph probabilities of feature vectors

I have a lot of feature vectors in the form of: v1=[x0, x1, x2, x3, x4] where x0, x1, and x2 can take binary values. either 0 or 1 x3 and x4 can take values from 0 up to 9 I have a lot of vectors ...
0
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0answers
42 views

stochastic network optimization

I'd like to optimize the flow of materials through a network. There are vertices (i.e. physical locations) and edges (i.e. links between the physical locations). Inputs: locations transactional ...
0
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0answers
10 views

Why do chordal graphs not lose conditional independences when its transformed from undirected to directed to factor graphs and around?

When chordal graphs are used to model probability distributions, why is it that they do not lose conditional independences when its transformed from a undirected to a directed to a factor graph and ...
0
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0answers
11 views

Why are chordal graphs special for inference in the context of Probabilistic graphical model?

I was trying to make a list of the reasons of why chordal graphs are important or interesting in the context of inference and probabilistic graphical models. Some of the reasons I have so far are: ...
2
votes
0answers
24 views

Efficient algorithm to enumerate all member DAGs of a Markov equivalence class

I'm working on a research project involving Bayesian networks. BNs are directed acyclic graphs (DAGs) used to compactly represent joint distributions of variables. In many cases, multiple DAGs can ...
0
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0answers
16 views

When would Probabilistic Graphical Model be more useful compared to other commonly used models?

When would PGMs be better compared to other classification algos like DT, or LR? I see that it will be better if there are relationships / dependencies between the features. Are there any other ...
0
votes
1answer
23 views

Factor graph vs Factor graphical model

In inference we use the terms undirected graphical models and directed graphical models. Why do we say factor graph instead of factor graphical models?
1
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1answer
37 views

How to perform Cross-Validation for glasso to select lambda in R

I am using glasso for variable selection. To get the best possible value of lambda cross validation is recommended. However, I am not able to find how to perform cross validation for glasso in R. ...
0
votes
2answers
67 views

What is the name of this type of tree / graph / representation?

I have come across a tree like way to organize variables and I wonder if anybody knows the name. A colleague thinks it has a "Japanese sounding name" (sorry by no mean I wish to be derogatory). So ...
2
votes
1answer
40 views

An example of r.v.s such that their distribution has more (conditional) independencies than their directed graphical model

I was trying to form an example where I had 3 r.v.s such that the distribution describing them had more conditional independencies or independencies than the directed graphical model corresponding to ...
0
votes
1answer
41 views

How does explaining away cause problems for learning?

In one of his lectures Geoff Hinton explains that a big problem of sigmoid belief nets is the explaining away phenomenon. I didn't fully understand this. I see that the induced width of the graph ...
0
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0answers
47 views

How to interpret residual vs fitted values plot with clustered points

I am performing a multiple linear regression and I have a plot of the my first two explanatory variables vs the residuals and also a plot with the residuals vs the fitted values. I am not quite sure ...
0
votes
1answer
32 views

Question of unary term (data term) of the graph cut method

I am trying to apply graph cut method for my segmentation task. I found some example codes at Graph_Cut_Demo. Part of the codes are showing below ...
1
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1answer
46 views

In Probabilistic Graphical Models, what does it mean that r.v. X influences r.v Y?

I wanted to pin down what the intuitive phrase: r.v. X influences r.v. Y as precisely and as rigorously as I could, and wanted to check if my interpretation was correct and complete with the ...
0
votes
1answer
42 views

Why do we need undirected (Markov) graphical models?

I understand the modular nature of directed models, and that each node captures a conditional probability. But why do we need undirected models? As far as I can see they lack intuition in that the ...
1
vote
1answer
20 views

Gaussian MRF/Markov Network: the zero precision = no connection?

Gaussian MRF in Gaussian information form: edge potential: $exp(\frac{-1}{2} y_s\Lambda_{st} y_t)$ node potential: $exp(\frac{-1}{2} y_t\Lambda_{t} y_t+\eta_ty_t)$ Why: precision parameter ...
2
votes
1answer
56 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
83 views

about the definition of bayesian network

In this PDF http://people.csail.mit.edu/yks/documents/classes/mlbook/pdf/chapter2.pdf page 5 says: Given a set of functions $f(x_i,pa(x_i))$ non-negative and sum to 1, we define a joint probability ...
1
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1answer
30 views

Hidden Markov Model with conditional observations

I am looking for a research paper that basically describes a hidden markov model that has multiple observations, and some observations that have conditional dependencies. For example, please consider ...
0
votes
1answer
43 views

simply demostration on conditioned probability

I don't find the answer of this simply problem: how can I algebrically demonstrate that, with 3 variable A,B,C $ \sum\limits_C P(B|C)P(C|A)=P(B|A)$ under condition that $P(A,B,C)=P(A|C)P(B|C)P(C)$ ...
1
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0answers
28 views

How are deterministic/logical nodes represented mathematically in directed graphical models?

Various software for performing inference on graphical models support logical(a.k.a deterministic) nodes. PyMc, Winbugs, Smile are the ones I'm aware of. Based on the different inference methods ...
3
votes
2answers
75 views

Learning to map vectors to vectors

Say we want to learn a function: $f(\mathbf{x} \in \mathbf{R}^p) \rightarrow \mathbf{y} \in \mathbf{R}^q$ where $\mathbf{x}$ and $\mathbf{y}$ are vectors representing time series. We have multiple ...
0
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1answer
38 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, ...
0
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0answers
42 views

Log linear models advantages

What are the advantages of log linear representation in opposite of table representation? Is it simply computational issue ( avoid overflowing)? For example, in a markov network A-B we can represent ...
2
votes
1answer
53 views

What is the correct definition for completness in d-separation in directed graphical models?

I was reading Koller's book of probabilistic graphical models and in section 3.3.2 she discusses what properties should hold for d-separation as a method for determining independence. She tries to ...
2
votes
2answers
135 views

What does the notation $(\textbf{X} \perp \textbf{Y} , \textbf{W}\mid \textbf{Z})$ mean?

I was reading Koller's and Friedman's Probabilistic Graphical Models book and became confused about some of its notation because of a set of notes that either contradict it or express it differently. ...
1
vote
1answer
89 views

LASSO or other regularized regression with censored (missing) data

Here is my problem. I am looking at various time series curves. Let's call them total spend aggregated over all customers on various products versus time. At any given time, I want to predict the ...
1
vote
1answer
493 views

Smoother lines for ggplot2

This question probably has a simple solution, still the thing is I've written a code to plot mortality in 2 different groups and that is, death in obese patients vs not obese. Now their are 2 groups ...
0
votes
1answer
23 views

Graphical Model Equivalent of Matrix Pseudoinverse

The may sound like a strange question but I was wondering if a Pseudoinverse of a matrix could be found using SVD whether there was a graphical modelling equivalent that could be used to estimate the ...
1
vote
1answer
34 views

Markov blanket conditional distribution derivation

I am trying to derive the formula for the conditional distribution for a variable in a Bayesian network: $$p(x_j|x_{-j})=p(x_j|x_{pa(j)})\prod_{k\in ch(j)}p(x_k|x_{pa(k)})$$ I understand D-separation ...
0
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0answers
46 views

Assumptions implied by “pairwise marginal” parameterization of MRF

I'm trying to understand the assumptions of different parameterizations in a Markov network. In this case, I'm trying to understand the assumptions (and effects) that result from parameterizing ...
2
votes
1answer
47 views

What is max-sum / max-product variant of loopy BP computing?

In (Nowazin and Lampert, Structured Learning and Prediction in Computer Vision, p. 29.), they say that in the max-sum variant of loopy belief propagation, the "variable max-beliefs are no longer ...
1
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0answers
26 views

How to design an energy function in a factor graph model?

When designing a factor graph, the designer needs to specify the structure of the graph (encoding independence assumptions), and the form and parameterization of the energy functions for the ...
1
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0answers
14 views

Does anyone know how to make this infograph with R? [closed]

This picture was posted on a Brazilian newspaper called "Folha de São Paulo". The graph relates each candidate's name with their allied political parties
0
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0answers
34 views

Possible inferences from a graph pattern

So, I had a weighted dynamic graph having info about 10 consecutive timesteps ( basically 10 files ). Now, I had to mine out patterns in the weight and structure of the complete graph. I did that. The ...
0
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0answers
51 views

A Graphical Model for Fellegi-Sunter Record Linkage

I am trying to understand the Fellegi-Sunter Probability Model for Record Linkage problem. I am following the thesis at: http://www.inf.ed.ac.uk/publications/thesis/online/IM080663.pdf in order to ...
1
vote
1answer
62 views

Difference between graphical model and markov chain

Representing causality using fuzzy cognitive maps presents a cognitive model which is a graphical model consisting of weighted directed graph. To me it looks like a state transition machine. Can ...
2
votes
1answer
84 views

Can Someone Explain How Factor Multiplication Works with Factor Graphs?

I'm taking the Probablistic Graphical Model course here: https://class.coursera.org/pgm-003/ This class uses the concept of Factors extensively with regards to graphical models: ...
0
votes
1answer
46 views

Explaining away

I am brushing up on graphical models, and doing the following problem 3.3 from the book PGM by Kophler. An alarm A can be set off by either Burglary B or earthquake E. Prove that if $P(a^1| b^1, e^1) ...
0
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0answers
122 views

Example on Hidden Markov Model

I was studying Hidden Markov Model(HMM) recently. I was looking to cross check what I understood. I found a code on Forward-Backward and Viterbi is given in simple Python terms in Wikipedia. I ...
1
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0answers
41 views

Python alternative to Factorie

Is there alternative / replacement in Python of Factorie (http://factorie.cs.umass.edu/index.html) - In particular I am looking for a tool in Python that allows me to create arbitrarily structured ...
0
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0answers
73 views

graphical methods / deep architectures for collaborative filtering

Having read "Restricted Boltzmann Machines for Collaborative Filtering" (Salakhutdinov et. al. 2007), I'm wondering if there has been any follow-up work on applying graphical and/or deep architectures ...
1
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0answers
38 views

How to run two motion plots together in R? [closed]

I'm doing a real recreation of the annual tree growth from tree-ring measurements, and I have many problems working with graphs and their margins. I have done the two graphs separately and these work ...
1
vote
1answer
70 views

normalization in max-sum algorithm (loopy belief propagation)

I was implementing the max sum algorithm for a general graph (i.e., the ones with a cycle). I updated the messages as indicated in ...
0
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0answers
20 views

Why calculating partition function is important in graphical models?

Consider a graphical model as specified at [1] Eq (1) and (2). Why calculating the partition function is important in graphical models? Why do we need it? I was under the impression that people ...
0
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0answers
88 views

Difference between fuzzy graphical network , Markov model and Bayesian network

Referring to this answer Difference between Bayesian network and neural network and causal inference, I have come across other graphical models (1) Fuzzy Cognitive Map and (2) Neuro-Fuzzy (3) Fuzzy ...