Questions tagged [bayesian]

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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: ...
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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 ...
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6 views

How can we estimate from data the probabilities for naive Bayesian classifier? [closed]

How can we estimate from data the probabilities for naive Bayesian classifier? I don't really understand this question, does it mean, how probable it is that the features are independent?
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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 ...
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1answer
67 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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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 ...
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60 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/...
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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 ...
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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 ...
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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, ...
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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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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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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 ...
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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 ...
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1answer
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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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23 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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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 ...
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20 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 ...
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2answers
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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 ...
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1answer
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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 ...
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1answer
58 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 ...
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Hidden markov model to estimate confidence in binary time series

I have binary time series representing active/inactive states eg. ...
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1answer
43 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. ...
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77 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? ...
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459 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) ...
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1answer
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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 ...
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1answer
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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
353 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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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
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the probability distribution of dependent variables

There are three variables, X3 is a function of X1 and X2, ...
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1answer
477 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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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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20 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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34 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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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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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
281 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
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 ...
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1answer
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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
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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
285 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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1answer
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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
118 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
39 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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1answer
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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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595 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/...