Questions tagged [bayesian]
The bayesian tag has no usage guidance.
64
questions
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29 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
14 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
10 views
Objective, surrogate and selective functions?
I am trying to understand how Bayesian Optimization works, Almost every blog which I am going through mentions objective function, surrogate function, selective function.
I am having trouble ...
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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 ...
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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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22 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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4 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 ...
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0answers
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 ...
1
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2answers
31 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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0answers
47 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
51 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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0answers
16 views
Hidden markov model to estimate confidence in binary time series
I have binary time series representing active/inactive states
eg. ...
1
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1answer
36 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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0answers
34 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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0answers
221 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
53 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
176 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
25 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
224 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
43 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
14 views
the probability distribution of dependent variables
There are three variables, X3 is a function of X1 and X2, ...
2
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1answer
287 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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17 views
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
26 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
19 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
20 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 ...
3
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2answers
44 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 ...
6
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2answers
2k 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
217 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
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1answer
57 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
88 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
43 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
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1answer
207 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
36 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
105 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 ...
2
votes
1answer
38 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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0answers
34 views
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 ...
2
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1answer
63 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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0answers
18 views
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
477 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
72 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 ...
0
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1answer
71 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
108 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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0answers
93 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.
...
1
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1answer
468 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
24 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 ...
2
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0answers
345 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
47 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 ...
