Questions tagged [bayesian]
The bayesian tag has no usage guidance.
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Bayesian models
Can someone tell me more about Bayesian models and how Bayesian networks used in Machine learning. Also some good references to help me learn more about the same would be great.
Thanks in advance.
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When to use BayesianSearcCV and how it works?
Can somebody highlight when to use BayesianSearchCV and how it works? I have seen the implementation of same on kaggle and wanted to explore it further.
Below is the link where the implementation ...
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LSTM: Converting to Bayesian Deep Neural Network
Starting from Yarin Gal's research paper on using Dropout as a Bayesian Approximation (https://arxiv.org/pdf/1506.02142.pdf), I am trying to apply this concept to my Sequence Prediction model. My ...
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Bayes posteriograms
My main objective is to predict the posterior probability of an individual belonging to one of the classes, using Bayes theorem. The information I have is:
value of the data point
mean and stdev of ...
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2answers
77 views
How do Bayesian methods do automatic feature selection?
Someone asked me this question and I do not know I answered it correctly.
I answered the question in the following way: One type of Bayesian method is Bayesian inference and feature selection has to ...
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0answers
11 views
the joint distribution for graphical models with circles
For the following graphical model, what should be the joint distribution of P(X1,X2,X3,X4). Comparing with normal Bayesian network, x2 and X3 are correlated as well.
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3answers
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Why prior in MAP could be ignored?
A posterior $p(\theta\vert x) = \frac{p(x \vert \theta)p(\theta)}{p(x)} $
Many materials say that since the $p(x)$ is a constant, the $p(x)$ can be ignored. Thus, $p(\theta\vert x) \propto p(x \vert ...
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1answer
12 views
the probability distribution of dependent variables
There are three variables, X3 is a function of X1 and X2, ...
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1answer
112 views
Effect of outliers on Naive Bayes
Are Naive Bayes algorithms affected by outliers in the data? Suppose there is a data set, does one need to remove outliers before applying Naive Bayes?
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How to use SLAM on other sensor other than camera?
I have a sensor that reads electromagnetic field strength from each position.
And the field is stable and unique for each position. So the reading is simply a function of the position like this: <...
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0answers
19 views
PyMC3: how to efficiently regress on many variables?
I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to ...
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13 views
How do I combine two electromagnetic readings to predict the position of a sensor?
I have an electromagnetic sensor and electromagnetic field emitter.
The sensor will read power from the emitter. I want to predict the position of the sensor using the reading.
Let me simplify the ...
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0answers
16 views
Bayesian Neural net with non probibalistic Data?
Is it possible to construct a Bayesian Neural Network without Probability Distributions as dependent Variable for purpose of predictive modeling?
I mean, if id like to Infer on a Specific Value, like ...
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1answer
16 views
Bayesian optimisation in deeplearning
Has anyone tried using Bayesian optimisation to get best learning rates, and other hyperparameters for deeplearning.
How to change the parameters between the training. Any examples on callbacks?
Can ...
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2answers
777 views
What makes a Tree-Structured Parzen Estimator “tree-structured?”
From what I understand the Tree-Structured Parzen Estimator (TPE) creates two probability models based on hyperparameters that exceed the performance of some threshold and hyperparameters that don't.
...
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1answer
112 views
How to do hidden variable learning in Bayesian Network with Python?
I learned how to use libpgm in general for Bayesian inference and learning, but I do not understand if I can use it for learning with hidden variable.
More precisely, I am trying to implement approach ...
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1answer
51 views
How to compute the maximum likelihood hypothesis?
The Bayes theorem states that:
\begin{equation}
P(h|D) = \frac{P(D|h)P(h)}{P(D)}
\end{equation}
where $D$ is the dataset and $h$ is an hypothesis from the hypothesis space $H$. Now (I'm not sure so ...
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1answer
42 views
Estimating class prevalence in unlabelled data after predicting labels with a binary classifier
I'm looking to get an estimate of the prevalence of 1's (i.e. the rate of positive labels) in a very large dataset that I have. However, I am hoping to report this percentage as a 95% credible ...
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1answer
37 views
Limits of using a normal distribution in Bayesian inference
When applying a Bayesian inference method such as Gaussian Process Regression (GPR), the assumption of a prior and likelihood function following a normal distribution is inherent. One can use an ...
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1answer
92 views
Linear Discriminant Analysis + bayesian theorem = LDA classifier??
I am new to machine learning and as I learn about Linear Discriminant Analysis, I can't see how it is used as a classifier.
I can understand the difference between LDA and PCA and I can see how LDA ...
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0answers
24 views
Probability of event given two depandant events
Is there anyway, to compute the probability of the event given two dependent events?
I know that Bayes can help if those events are independent, but what if condition events are dependent?
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1answer
82 views
How to best estimate the coefficients of a confusion matrix in case of strong class imbalance?
I have a trained binary classifier (forget about how this was trained and think of it as a magical black box) and I would like to measure its classification performance (e.g. compute a confusion ...
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1answer
26 views
Where can we find the application of bayes's theorem in Bayesian optimiation with gaussian processing
I am trying to learn bayesian optimisation by following this tutorial.
However, until now I don't get the relation between bayes's theorem to the gaussian process formalism.
Any ideas?
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When to model a problem by using the Bayes' theorem?
I have a labeled training dataset where each observation has a sentence either in English or in French as its predictors and its label (target value) is whether this sentence is English or French. The ...
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71 views
Linear regression model with Automatic relevance determination VS sequential thresholding or Lasso?
Background
Linear model with Automatic relevance determination is doing almost the same thing as sequential thresholding linear squares. And it is close to Lasso. I found both ARD and LASSO are ...
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65 views
Is there already regularization in optimizing KL divergence?
Background
Recall that
$$\textrm{KL}[q(\theta) \rVert p(\theta | x)] = \int q(\theta) \log \dfrac{q(\theta)}{p(\theta|x)} d\theta$$
It is equivalent to maximize the EBLO, aka, variational lower ...
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26 views
Difference between MOE and Spearmint?
MOE seems to be a plain Bayesian optimization.
Just curious if anyone knows about the difference.
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1answer
61 views
How Do Bayesian Methods in Machine Learning Help With the Problem of Limited Data? Can This Be Used for Image Classification/Recognition? [closed]
When reading about machine learning, I've often come across information stating that Bayesian methods in machine learning are effective when you only possess a limited amount of data. As someone who ...
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Algorithms behind “smartlinks” for online traffic distribution
Several traffic networks offer "smart links", where you can send online traffic, and the traffic will be sent to the most profitable segment. These networks include Monetizer, Glize etc.
I.e. if you ...
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0answers
21 views
Optimality of Bayes classifier
I've studied an introdution to the bayer optimal classifier and i've understood how it works.
What isnt clear to me is why it's considered optimal, and what it means to be optimal in this case.
Can ...
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0answers
326 views
Correct calculation of BIC (Bayesian Information Criterion) to determine K for K-Means
I am trying to calculate BIC in python. In python, there is no inbuilt library for computing BIC. I referenced the following link to compute variance and BIC further:- https://stats.stackexchange.com/...
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0answers
54 views
How to integrate out hyperparameters of Gaussian process for Bayesian optimization?
I read this paper (https://arxiv.org/pdf/1206.2944.pdf) discussing about practical issues of Bayesian optimization and they mentioned that integrating out hyperparameters of Gaussian process using ...
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1answer
54 views
How to calculate the Probability for the Unconditional Node in the Bayesian Belief Network?
In the popular example for Bayesian Belief network Burglary Alarm how is the probability for burglary P(B) and earthquake P(E) calculated as 0.001 and 0.002 respectively?
Is it an assumption made or ...
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2answers
76 views
Statistical inference on a very small datasets
I have been working with machine learning for about a year now, but mostly with large datasets. However, I am currently working on a problem with a very small dataset. Here is my problem: I am ...
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75 views
Custom regularisation for logistics regression
My understanding of l2 regularisation:
Weights of the model are assumed to have a prior guassian distribution centered around 0. Then MAP estimate over data adds an extra penalty in cost function.
...
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1answer
315 views
Creating a posterior distribution for classic coin flipping in python using grid search
I'm reading the book "Bayesian Analysis with Python" and the author provides some python code designed to show the grid search method of obtaining an approximate posterior distribution for the classic ...
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0answers
23 views
How to interpret long equations in Deep Learning papers
For eg. I've been studying a paper on Recommender systems using collaborative deep learning and I've just started learning.
The paper revolves around the NN representation as shown below
The ...
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286 views
Bayesian linear regression / categorical variable / Laplace prior
I'm trying to do feature selection in the bayesian framework with a Laplace prior with the following code in Python;
Code:
...
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0answers
33 views
RNADE vs MDR vs BART vs Bayesian Linear Regression
I'm looking at a collection of problems where I need to forecast the probability of a continuous variable. My dependent variables are a combination of categorical and continuous. It looks like there ...
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1answer
67 views
ML algorithm where variable importance depends upon other variables - specifying conditionality
I am designing an algorithm that can detect cheating in a game of chess.
I have a database of players who have been flagged by moderators as cheating, along with a number of game-level ...
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1answer
224 views
Replacing missing value by class conditional mean
I have two classes, $p(x|y=0)$ and $p(x|y=1)$ with ${{\mu }_{0}}$ and ${{\mu }_{1}}$ as mean and shared covariance matrix $\Sigma $. Now, I have a missing feature ${{x}_{n}}$ for a particular ...
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137 views
How to use pymc3 to sample the mean of a Pareto random variable?
I Have a variable which is Pareto-ly distributed 'x', with unknown alpha and m.
I want to find out the distribution of its mean, so I use the following model:
...
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0answers
49 views
information leakage when using empirical Bayesian to generate a predictor
Consider the following problem: I want to predict the next bat of a set of baseball player. I have a training data set, where it contains the historical bat records (0-1 encoded, which is our target ...
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2answers
203 views
Ensemble learning [closed]
I am currently working to build a mathematical model to predict the stock market. I learned that the best way to do such thing is no longer to make one big best model, but rather to gather several ...
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75 views
How to Combine Multiple Empirical Bayes Estimations
I've recently purchased and read the excellent book, Introduction to Empirical Bayes by David Robinson. As someone who is not a trained statistician, but has an interest in data science, I found this ...
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1answer
699 views
Why the estimated Lasso coefficients of almost all variables are equal to zero?
I would greatly appreciate if you could guide me. In fact, I used "Bayesian Optimization " to tune hyper-parameters of Lasso but the estimated Lasso coefficients of almost all variables are equal to ...
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0answers
422 views
Implement gaussian mixture model with stochastic variational inference
I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper.
This is the pgm of Gaussian Mixture.
According to the paper, the full algorithm of ...
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1answer
34 views
Finding the most important questions from a questionary-> results
Lets assume that I have 5000 articles and I create the TF-IDF of these articles.
Now I ask som people to answer 30 questions and I create the TF-IDF of these answers from the IDF of the articles and ...
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1answer
468 views
Modeling uncertainty from Logistic Regression
Logistic regression is a part in a simulation pipeline that I use for some scenario analysis. The dataset that this is based on is not small but relatively noisy, and only one explanatory variable/...
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1answer
99 views
Is deep learning a must in a Data Science MSc programme? [closed]
I am reading the programme outline of this two-year MSc in Data Science and I found that it has no deep learning content (as in many other european ones). I am no expert but as far as I've seen I ...
