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1answer
20 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 ...
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1answer
22 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 ...
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0answers
14 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 ...
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0answers
25 views
+100

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 ...
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0answers
13 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 ...
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0answers
9 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
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0answers
28 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 ...
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0answers
21 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 ...
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1answer
31 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 ...
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1answer
43 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
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1answer
38 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) ...
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0answers
64 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 ...
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0answers
28 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 ...
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0answers
46 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 ...
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0answers
29 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 ...
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1answer
30 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 ...
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0answers
13 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 ...
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0answers
46 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 ...
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2answers
55 views

Modeling user behavior in a system

Consider a system, where each user enters the system, performs a series of predefined actions, and then exits. For instance, consider a system with 5 predefined action. The action log of some user is ...
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1answer
20 views

Can graphical models represent independence besides conditional independence?

In a graphical model, two random variables are conditional independent given their common ancestors. Can graphical models represent independence besides conditional independence?
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3answers
210 views

Why are directed graphical models called Bayesian?

Why are directed graphical models called Bayesian,while those undirected not? Does that mean that Bayesian analysis is involved in the directed ones, and not in those undirected?
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0answers
14 views

Minimal I-maps induced by sets of scopes. Clarification needed

I have a question about Prop. 9.1 on page 307 in "Probabilistic Graphical Models" (link to google books) (Koller / Friedman). I don't see why $\mathcal{H}_{\Phi}$ is a minimal I-map: If I have only ...
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0answers
12 views

Neighborhood in Gaussian graphical models

A gaussian random vector $X$ can be represented using a graph where two nodes $a$ and $b$ are connected $\Leftrightarrow X_a$ is dependent on $X_b$ given all the remaining random variables. I have two ...
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1answer
16 views

Cycles in gaussian graphical models

I understand examples where two nodes can be dependent but conditionally independent given their common cause. For instance the common cause is high temperature, and the children nodes are high ...
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0answers
18 views

Maximizing expectations vs Mode maximization

In many statistical problems, I see the following formulation for maximizing rewards: Assuming that my total reward $R$ is the sum of individual rewards $R$: ...
3
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1answer
117 views

How to compute marginals in Sum-Product Networks?

This should be fairly easy, but for some reason i'm having hard time getting it to work and I've spent a long time trying to figure it out myself. In the last paragraph of page 4 of the original ...
0
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1answer
31 views

How do you calculate the amount of parameters needed to be estimated?

I don't quite understand this. A question was, pretend we have 4 predictors and all of them are binary - for the Naive Bayes method, how many parameters are there to estimate in the training step? ...
0
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1answer
38 views

factorGraph approximations: Splitting variables

I am reading Chris Bishop's chapter on Expectation Propagation and there is the bit on how approximating factor graphs as shown in the images. So, the original joint distribution can be written as: ...
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0answers
11 views

Determine if Clique tree is calibrated

A, B 0 0 50 0 1 10 1 0 20 1 1 50 2 0 15 2 1 15 The above table defines the clique potential $\phi_1(A,B)$. B C 0 0 62.5 0 1 12.5 1 0 12.5 1 1 62.5 The above table defines the clique ...
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0answers
42 views

Learning Conditional Random Fields using EM Algorithm (from unaligned data)

I am trying to learn CRF from the unaligned data in Natural Language Understanding application. There is one paper in this field which does exactly the same, Learning conditional random fields from ...
0
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1answer
25 views

How can a probability distribution P not factorize over a graph H when P satisfies the independencies implied by H

How can a probability distribution P not factorize over a graph H when P satisfies the all the global independencies implied by H? Here's an example: Let $X_1, \dots X_4$ be 4 random variables that ...
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0answers
46 views

directed bayesian network and factor graphs

I have a directed bayesian given by the figure below. In the figure the circles are random variables and the shaded ones are observed. The rectangular nodes are constants representing the hyper ...
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0answers
21 views

How different is training a factor graph with discriminative features?

Many of the people define graphical models with factors, each with 'conditional probability tables' (CPT) and perform inference on them. But more realistic case is when you can't define full ...
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2answers
121 views

Why use factor graph for Bayesian inference?

I don't understand why converting a Bayesian network into a factor graph is good for Bayesian inference? My questions are: What is the benefit of using factor graph in Bayesian reasoning? What ...
1
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2answers
85 views

Does this graphical model describe a hidden Markov model?

I'm facing the problem of visual tracking in computer vision. I have some observation (image blobs by background subtraction) produced by some moving object, and the task is to infer the state ...
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0answers
14 views

Loss specific belief propagation

We know standard Belief Propagation finds the parameters which maximize probability of posterior. Is there any way to use BP, for loss specific inference? For example, let's say someone wants to ...
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0answers
119 views

Easy to follow tutorial on using Markov Random Fields for classifying pixels in gray-scale images

I am trying to learn how to use Markov Random Fields for classifying pixels in an image. Could someone please direct me to a simple tutorial demonstrating how this is done. The tutorial needs to ...
3
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1answer
88 views

Graphical Models and Explaining Away?

So I have fundamental confusion about graphical models. Suppose the following graphical model is given: Now the question is do we have the following equality?: ...
0
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1answer
68 views

Bayesian network to factor graph

I have a bayesian network with conditional probabilities as given by the diagram and I have converted it to factor graph. I just sort of read about factor graphs. Can someone be kind enough to let me ...
3
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2answers
171 views

What is the relationship between graphical models and hierarchical Bayesian models?

I've searched a good bunch of literature but have failed to find an exact distinction between the two. My impression is that in the Machine Learning literature you'll find allusions to hierarchical ...
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0answers
14 views

How to connect nodes in full conditional model of GRN?

I have problem in intuitive understanding of full conditional model in building gene regulatory network. it's basically asking that "can correlation between two genes be explained by all other genes ...
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4answers
133 views

Do edges in directed acyclic graph represent causality?

I am studying Probabilistic Graphical Models, a book for self-study. Do edges in a directed acyclic graph (DAG) represent causal relations? What if I want to construct a Bayesian network, but I am ...
2
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1answer
128 views

Difference of Prediction between Graph Representation and Data Matrix Representation

In data mining, data can be usually represented in different forms such as records of a matrix, graphs or ordered data. While we find in research different papers addressing methods or solutions for ...
1
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1answer
96 views

How to quickly build an interactive diagram modeling relationships between nodes from file input?

I hope this finds someone with the right expertise here. I am currently working with a data set on product information that has - a defined search tree structure - product types - synonyms for ...
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0answers
31 views

Probabilities from Graphical Model

I am trying to derive some probabilities from the following graphical model and I am having some difficulty. The model is actually a complete bipartite graph with two sets of nodes C and W. The graph ...
0
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0answers
19 views

How can I model random graphs (trees) with deterministic constraints?

I have been trying to represent a tree structure as a random graph, but so far my research has led to variations of random graph models and I can't see studies which may help me leverage some of the ...
2
votes
1answer
116 views

At what point does LDA (Latent Dirichlet Allocation) not make sense to use?

I was wondering if anyone could provide some intuition about when the "documents" in LDA are too small for it to provide any benefit. For instance, I have seen papers where people have you used ...
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0answers
25 views

When to use Bayesian parameter learning, when others?

Is there any general suggestion as when to use Bayesian, and when other inference approaches?! For example in the case of CRFs, when to use ML [1] and when to use Bayesian approach [2] ? [1] ...
3
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0answers
40 views

Bounds (or model) for estimating probability from generating process

I was curious if there was any bounds or approaches to getting good estimates for a probability from the following generating process. Suppose we have 2 sets of objects $A$ and $B$ where both sets ...
1
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1answer
100 views

Parameters and parameter estimation in graphical models

I try to understand parameter estimation and learning problems at Graphical Models, especially in directed ones (Bayesian Networks). But first of all, I try to understand what exactly a parameter ...