Questions tagged [bayesian]
The bayesian tag has no usage guidance.
78
questions
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1answer
28 views
why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?
Im trying to understand why naive is needed in Naive Bayes and everyone says Naive Bayes assumes the input features (predictors) are not correlated hence they are not dependent on each other .
i want ...
0
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0answers
24 views
Bayesian Network - a practical example of marginal probability calculation?
I was watching an online course on the topic Bayesian Networks and I have a question regarding the calculation of marginal probabilities.
Hier is the given network:
and the corresponding course video ...
2
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0answers
14 views
If I use Gibbs sampling with a Bayesian model, what do I have to check is memoryless?
Right now I am trying to better understand how Bayesian modeling works with just the basics. I found through reading tutorials that some very basic Bayesian models like Bayesian Hierarchical Modeling ...
2
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2answers
35 views
Is k-means with Mahalanobis a valid option for clustering?
I want more info into if k-means with Mahalanobis distance is a mathematically/methodologically correct option for datasets with different variance clusters.
The steps are:
Create aggregate datasets (...
1
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0answers
26 views
GMM with Dirichlet prior
I'm learning about the variational inference - mean field approximation on from this online course DeepBayes2019 page 30
The probabilistic model is written as follows:
$p(X, Z \mid \pi, \mu, \lambda) ...
3
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3answers
185 views
What is the meaning of likelihood?
I am studying Bayes probability applied to machine learning, and I have encoutered the concept of likelihood, which I don't understand.
I have seen that the Bayes rule is:
$P(A|B)=\frac{P(B|A)P(A)}{...
0
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1answer
50 views
How to make model for Multivariate timeseries using tensorflow probability structural model?
I have done modelling for Univariate timeseries but while using multivariate time series ( independent features) not able to ...
0
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0answers
12 views
Bayesian/Frequentist “philosophies” in relation to common ML models
Starting to think about questions tout could come up in a data science job interview, there is this one question that is bugging me since i don't have a deep background in statistics.
I have a ...
0
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0answers
12 views
what R and Python package can predict a word list with certain probability after one known word?
I have a lot of documents, I need to know after one know word, which words occurs with which probability. For example:
...
1
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0answers
10 views
Derivation of Bayes classifier in Murphy's book
I am reading Kevin Murphy's Machine Learning book (MLAPP, 1st printing) and want to know how he got the expression for the Bayes classifier using minimization of the posterior expected loss.
He wrote ...
2
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0answers
105 views
What are the tradeoffs between Bayesian Deep Learning and Deep Gaussain Processes?
I understand the differences between Deep Gaussian Processes(DGPs) and Bayesian Deep Learning(BDL): DGPs are essentially feed-forward neural networks where each node is a Gaussian Processes, which BDL ...
3
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1answer
71 views
How to update the posterior belief when we are observing a stream of correlated data from a fixed but unknown data source
I want to build a [probabilistic] model that aims to infer the true value of an unknown categorical variable, $y \in \{1,2,..., K\}$.
We have a dataset $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
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0answers
6 views
Good introductory reference for Bayesian Non-parametric (Dirichlet Process / Chinese Restaurant Process)
I am looking for a recommendation for basic introductory material on Bayesian Non-parametric methods, specifically Dirichlet Process / Chinese Restaurant Process. I am looking for material which ...
0
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0answers
112 views
Tensorflow Probability Example : Gradient Computation Error
I am trying to run example code from tensorflow Probability for Dirichlet Process Mixture Model (https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/...
0
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0answers
10 views
Bayes factor values
I have two samples A and B with continuous values such as results students. I want to calculate the baysian t test to get the Bayes values. I get the Bayes value of 0.16,so does it mean the null ...
0
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0answers
24 views
Bayesian test for classification problems?
I am using two classification algorithms in Weka i.e. Logistic Regression and Naive Bayes and want to know which algorithm has better performance? I need a statistical test like Bayesian, so which ...
1
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0answers
44 views
how to use Bayesian theorem and probabilistic analysis? [closed]
could someone help me to solve this problem please ?
Your prize Rapid Ripe tomato plant has flowered and is ready to start producing fruit. If all goes well, ...
0
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0answers
9 views
Is it possible not to make all mcmc process every time when we get new observation without big quality decrease?
I have a service that has to give a probabilistical distribution for some value in the nearest future. It works on MCMC-sampling algorithm (Stan sampling). Each time when this service is asked for an ...
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0answers
15 views
Help in interpreting the results of the A / B test Bayessian approach for non binomial data
I would like to carry out an A / B test based on data regarding the profit from a given user. I found an interesting library for such tests https://github.com/tcassou/babtest.
Now I would like to ask ...
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0answers
6 views
Maximum A posteriori estimation of sequential single parameter
Assume you have a temperature sensor that outputs one single reading every minute. After 1000 reading you put all these values together and build a prior pdf which happens to be Gaussian. Now, you get ...
0
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0answers
9 views
how to consider sampling when trying to inference exponential parameter
consider this case:
There is a price rate for a certain product that changes throw time,
The price rate is changed every x minutes.
This price get sampled/observed in a non-uniform distribution
...
0
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1answer
12 views
what is the representation/meaning/implication in real life of $P(\text{+})$ in the wiki Drug testing Example about Bayes' theorem
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to ...
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0answers
25 views
Derive Bayes Theorem using Deep Learning
This may be a silly question, but has anyone tried to use deep learning to derive the actual theorem of Bayes?
So, given some data, is it possible to show that a network has learned Bayes Theorem as ...
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0answers
8 views
Bayes by Backprop
Since the bayes by backprop algorithm is modeling weight uncertainty by drawing samples from from a gaussian distribution, I wondered what the biggest difference to the following approach would be:
I ...
0
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0answers
22 views
Determining users that have been sent too many emails
The experiment is a set of users in a marketing program. We are sending campaigns to the user and want to figure out whether the user is being sent too many emails or not.
Suppose you have a dataset ...
1
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2answers
41 views
Parallel hyperparameter optimization techniques?
Most hyperparameter optimization technique want to evaluate points one by one.
I have an expensive optimization problem, but i can run hundreds of evaluations in parallel.
The dimension of the problem ...
2
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1answer
69 views
MCMC for finding Bayesian Neural Network
Is someone familiar with such an approach:
Suppose I want to build a bayesian neural network, with distributions over my parameters instead of point estimates. First I train my network with standard ...
0
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1answer
60 views
How to test dev set on Time Series data via forecasting
I'm implementing $3$ Bayesian Deep Learning models (links below) for my masters. I'm supposed to test them on a civil engineering time series data.
My models ...
0
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0answers
19 views
Hidden markov model to estimate confidence in binary time series
I have binary time series representing active/inactive states
eg. ...
1
vote
1answer
51 views
Using predicted probabilities and bayesian inference to update beliefs
I'm currently working on a project to predict the likelihood of an outcome. I'd like to implement a system where the belief the event will happen is updated after running the model on new data. ...
0
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0answers
81 views
Different sensitivity analysis
I have a metabolic model written in python and I would like to do a Bayesian sensitivity analysis on it to see which parameter affects it the most. Is MCMC sensitivity analysis the same as Bayesian? ...
1
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0answers
564 views
Bayesian optimization for a Light GBM Model
I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choice) ...
1
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1answer
80 views
When to use BayesianSearchCV 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 ...
2
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1answer
517 views
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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0answers
27 views
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 ...
0
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2answers
442 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 ...
1
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3answers
44 views
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 ...
1
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1answer
17 views
the probability distribution of dependent variables
There are three variables, X3 is a function of X1 and X2, ...
3
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2answers
550 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?
1
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0answers
38 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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0answers
35 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 ...
4
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2answers
77 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 ...
7
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2answers
3k 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.
...
1
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1answer
296 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 ...
1
vote
1answer
58 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 ...
0
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1answer
102 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 ...
0
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1answer
52 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 ...
2
votes
1answer
346 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 ...
2
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1answer
41 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?
4
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1answer
129 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 ...
