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25 votes
Accepted

Looking for a good package for anomaly detection in time series

I know I'm bit late here, but yes there is a package for anomaly detection along with outlier combination-frameworks. The package is in Python and its name is pyod. It is published in JMLR. It has ...
Shankar Chavan's user avatar
8 votes
Accepted

Is the direction of edges in a Bayes Network irrelevant?

TL;DR: sometimes you can make an equivalent Bayesian network by reversing arrows, and sometimes you can't. Simply reversing the direction of the arrows yields another directed graph, but that graph ...
Robert Dodier's user avatar
7 votes

Looking for a good package for anomaly detection in time series

There are multiple ways to handle time series abnormalities- If abnormalities are known, build a classification model. Use this model to detect same type of abnormalities for time series data. If ...
Arpit Sisodia's user avatar
6 votes
Accepted

What is difference between Bayesian Networks and Belief Networks?

Both are literally the same. A Belief network is the one, where we establish a belief that certain event A will occur, given B. The network assumes the structure of a directed graph. The term Bayesian ...
Syed Ali Hamza's user avatar
4 votes
Accepted

Make the CNN to say "I don't know"

You could put a one-class classification model before your CNN. This would mean that you treat both your classes as one and then frame it as an anomaly detection problem. There are some different ...
Simon Larsson's user avatar
3 votes
Accepted

What's the difference between probabilistic programming such as pyro and belief networks?

A probabilistic program and a Bayesian Network are both ways of specifying probabilistic models. Any model that can be specified as a Bayesian Network can also be specified by a probabilistic program, ...
fritzo's user avatar
  • 236
3 votes
Accepted

Bayesian networks in scikit-learn?

If you wish to understand and use Bayesian networks, you can try OpenMarkov, an open-source tool. I recommend you having a look at its tutorial.
Francisco J. Díez's user avatar
3 votes

Pecularities of classification of Hidden Markov Models?

What you are looking for is called a metric (or distance, or similarity measure) for HMMs (some people would say SVM kernel for HMMs). Somehow, if you have a distance, you can cluster the HMMs. The ...
Eskapp's user avatar
  • 446
2 votes
Accepted

Are there Machine Learning Models for Networks?

Coming from the related field of measuring and predicting network security, I'd strongly suggest trying a time-series forecasting. I assume your data is time-stamp based (network congestion values, ...
mork's user avatar
  • 359
2 votes

R - Bayesian network for satisfaction survey data

I see the task is to understand the pattern rather than developing a prediction model. I would use regression trees rather than Bayesian network.
user3883642's user avatar
2 votes

Bayesian network for classification using PyMc or PyMc3

pymc will not provide you pretty sklearn-style .predict method for this case, however you can do it on your own. The idea is ...
arsenyinfo's user avatar
2 votes

What is the difference between a (dynamic) Bayes network and a HMM?

From a similar Cross Validation question follows @jerad answer: HMMs are not equivalent to DBNs, rather they are a special case of DBNs in which the entire state of the world is represented by a ...
xboard's user avatar
  • 378
2 votes

Applying bayesian methods to a simple neural network

Bayesian models specify priors to inform and constrain the models and get uncertainty estimation in form of a posterior distribution. Non-Bayesian Deep Learning computes a scalar value for weights and ...
Brian Spiering's user avatar
2 votes

Proceeding with various methods for news recommendation

Recommender Systems are a huge topic of its own right and goes without saying, with a lot of research going on. This book does a deep-dive into recommender systems and may not be something you want, ...
gokul_uf's user avatar
  • 141
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
Accepted

Probability of event given two depandant events

Let $A$, $B$, $C$ be three (potentially dependent) events. By the probability chain rule: $$ P(A,B,C)=P(A|B,C)P(B,C)=P(A|B,C)P(B|C)P(C) $$ So we can do \begin{align} P(A|B,C) &= \frac{ P(A,B,C)}{...
user3658307's user avatar
  • 1,020
2 votes

Bayesian networks in scikit-learn?

PyMC3 is a Python library build on top of Theano. And then there pymc3_models that adds a scikit-learn like API.
Soerendip's user avatar
  • 734
2 votes

Looking for a good package for anomaly detection in time series

Try Prophet Library Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects....
Pluviophile's user avatar
  • 3,878
2 votes

Does the Bayesian MAP give a probability distribution over unseen data?

Bayesian MAP gives a point estimate $\mathbf{w}_{MAP}$ for model parameter $\mathbf{w}$, which implies $p(\mathbf{w}|\mathbf{t},\cdots) = \left\{\begin{matrix} 1 & \mathbf{w}=\mathbf{w}_{MAP}\\ 0 ...
Esmailian's user avatar
  • 9,322
1 vote
Accepted

Undestanding Bayesian network with OpenMarkov

First off, I did not know OpenMarkov. Anyway, from its website it has a particular focus on learning Bayesian networks (Bayes nets). Thus, I assume your figure represents a Bayes net, yes. ...
John Q's user avatar
  • 56
1 vote

How to do hidden variable learning in Bayesian Network with Python?

While this model could be implementable in the libpgm library (it seems to have quite rigid interface tailored to several special models, though), it will not allow ...
Artem Sobolev's user avatar
1 vote

Looking for a good package for anomaly detection in time series

I found Matrix profiles useful, especially as they do not require much knowledge up ahead - not even the duration of the time frame. For what I understood they basically rely on autocorrelation. The ...
Eulenfuchswiesel's user avatar
1 vote

Are there Machine Learning Models for Networks?

A simple approach would be to use k-nearest-neighbors, where the distance metric is, in your case, "the number of road links away." The technique is described in chapters 2 and 13 of The Elements of ...
Ryan Zotti's user avatar
  • 4,149
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
1 vote

Which learning algorithms to use in what order - dimensionality reduction, bayesian network structure, regression?

Is there any "known" approach to such a problem, i.e. a way to determine the formula that connects the initial variables to the later variables that determine the outcome, and then further ...
krishnab's user avatar
  • 163
1 vote

How to calculate the Probability for the Unconditional Node in the Bayesian Belief Network?

As the comment says, Burglary and Earthquake only have prior probabilities in the Bayes net. These priors are not calculated from other variables/distributions. They are just assumptions in this ...
John Q's user avatar
  • 56
1 vote

Small amount of training data set for naive Bayes classifier for binary classification

I think 300 is a good enough size. Try doing split validation and see what kind of results you are getting. I implemented a naive bayes classifier with a dataset of 100 rows and the results were not ...
Vin's user avatar
  • 89
1 vote
Accepted

Small amount of training data set for naive Bayes classifier for binary classification

Actually in machine learning more data equals more accuracy but as you mentioned in the question you had 300 sample dataset.So, the classifier has the little room to decide whom it should select but ...
Sampath Madala's user avatar
1 vote

Reversed Naive Bayes - likelihood and parameter estimation

To elaborate on my comment, the first image depicts conditional dependence of $X_i | Y$ for $i \in 1,...,m$. Therefore, your likelihood for this model would be $$ \begin{aligned} P(Y = y) \times P(...
Jon's user avatar
  • 481

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