8 votes

Which tribe does Probabilistic Graphical Models fall under?

Probabilistic Graphical Models (PGMs) are: Connectionist: RBMs are PGMs and neural networks (source) Bayesian: Bayes Networks are bayesian (Wikipedia article) Symbolist: Markov Logic Networks (source)...
Martin Thoma's user avatar
  • 18.9k
3 votes

How to visualize optimization problems' feasible region?

You can plot the feasible region with python matplotlib and numpy libraries. ...
rubengavidia0x's user avatar
3 votes

Graph to display differences (or lack of) in multilevel categorical data

OK, here are my attempts with R & ggplot2 1 Simple stacked histogram 2 Dodged stacked histogram ~ bacteria 3 Dodged histogram ~ Culture 4 Dodged histogram ~ change 5 Grouped by number of ...
aivanov's user avatar
  • 1,510
2 votes

How to approach graphed data for a binary classification system?

Super comprehensive question. You are basically asking for tones of directions! I'd suggest to start with link prediction problem. So assume you have a directed/signed/weighted graph and you want to ...
Kasra Manshaei's user avatar
2 votes

Graphs demonstrating the structure of neural networks are very unclear

You are making one mistake which cascades on towards other mistakes. The multiple inputs are different features of the same sample. Let's say we want to regress house prices based on some features of ...
Jan van der Vegt's user avatar
2 votes

Which tribe does Probabilistic Graphical Models fall under?

Based on this paper (Qi, Szummer, and Minka. 2005. Bayesian Conditional Random Fields): In this paper, we propose Bayesian Conditional Random Fields (BCRF), a novel Bayesian approach to training ...
justhalf's user avatar
  • 123
2 votes

Which tribe does Probabilistic Graphical Models fall under?

Only Domingos knows for sure, since he invented this taxonomy, but I'd guess it would fall under "connectionists" (which he associates with neural networks), since graphs are all about connections (...
Emre's user avatar
  • 10.5k
2 votes

Libraries for Bayesian network inference with continuous data

You can use pymc3. I am pretty sure it works for all the 3 requirements. http://pymc-devs.github.io/pymc3/
ashudeep21's user avatar
2 votes

Why does a belief network need to be represented using a directed acyclic graph (DAG)?

A Belief (Bayesian) network is "defined" to be a DAG. A better question would be Why a distribution needs to be represented by a Belief network, i.e. a DAG? Dependency relationships can be ...
Esmailian's user avatar
  • 9,312
1 vote

How to visualize optimization problems' feasible region?

For Wolfram Language you may use ImplicitRegion, ContourPlot, and RegionPlot. For a ...
Edmund's user avatar
  • 705
1 vote

How to perform link prediction in text based relationship data

You are describing building a Probabilistic Graphical Model (PGM). The most commonly used Python library to build a PGM is pgmpy.
Brian Spiering's user avatar
1 vote

Why does a belief network need to be represented using a directed acyclic graph (DAG)?

Yes, we use DAGs to represent dependency relationships. We need directed graph because condition probability P(A|B) is not same as P(B|A). We assume it to be acyclic to get certain properties and ...
Preet's user avatar
  • 638
1 vote

Training a Graph model like an Artificial Neural Network

First, a note: don't let the graph structure make you think that this can be addressed with neural networks; this structure only suggests that this is a hierarchical model. Second, let's take into ...
noe's user avatar
  • 26.6k
1 vote
Accepted

Handling time series data with gaps

I'd represent actual repeated values differently from missing data. The latter is straightforward; the former I'd interpolate with Gaussian process regression. This way you'll be able to get error ...
Emre's user avatar
  • 10.5k
1 vote
Accepted

Viterbi-like algorithm suggesting top-N probable state sequences implementation

Apparently, I misunderstood your question. There are several methods for finding the k-best paths with extending versions of the Viterbi algorithm. My first advice would be to look at this question ...
Eskapp's user avatar
  • 446
1 vote
Accepted

Learning with dirichlet prior - probabilistic graphical models exercise

Your formula is correct, but the final computing is wrong. It should be: $\frac{10+87}{270+2000} \times \frac{10+100}{270+2001}$
Nhat Nguyen's user avatar
1 vote

Libraries for Bayesian network inference with continuous data

Also have look at Genie (GUI) and SMILE (Lib) from BayesFusion (formerly University of Pittsburgh). Academic usage is free, but registration required.
John Q's user avatar
  • 56
1 vote

Libraries for Bayesian network inference with continuous data

Not a library, but a interactive GUI based tool is "samiam" (Sensitivity Analysis Modeling Inference and More) from a research group at UCLA. I am not sure about your "continuous PDFs" requirement, ...
knb's user avatar
  • 602

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