Questions tagged [bayesian]
The bayesian tag has no usage guidance.
100
questions
2
votes
1answer
54 views
Really confused with characteristics of Naive Bayes classifiers?
Naive Bayes classifiers have the following characteristics-:
They are robust to isolated noise points because such points are
averaged out when estimating contiditional probabilities from data.
Naive ...
0
votes
1answer
13 views
How does bayesian optimization with gaussian processes work?
Could someone explain in simple words
what are gaussian processes
how does bayesian optimization work
and their combination?
0
votes
0answers
5 views
Estimating related metrics using Maximum A Posteriori
English is not my mother tongue; please excuse any errors on my part.
I've recently faced a problem for which I haven't found any solutions after investing a lot of time. Here is the summarized ...
0
votes
0answers
12 views
When to include LDA axis on a random forest analysis
I am using the R package abcrf and have the option of including LDA (linear discriminate axis) with my other statistics. If I include these I get a different answer to when I don't, so I want to know ...
3
votes
2answers
86 views
How useful is Bayesian Inference
Last few months, I had been exposed to Bayesian Inference in ML course
With further investigation, I come to place where there is MCMC technique to simulate the ...
0
votes
1answer
27 views
Update of mean and variance of weights
I'm trying to understand the Bayes by Backprop algorithm from the paper Weight Uncertainty in Neural Networks, the idea is to make a NN in which each weight has it's own probability distribution. I ...
2
votes
2answers
139 views
High Recall but too low Precision result in imbalanced data
I was training a model using XGBoost Classifier on a heavy imbalanced database with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature ...
0
votes
0answers
17 views
Is it a good idea to use the mean and standard deviation of coefficients from other models as my prior in Bayesian Regression?
I have a dataset that I’ve been playing around with for school I have gotten very good results with a bunch of methods (Ridge, Lasso, ElasticNet, SVM, Bagging, Stacking and NN even)
Now I’m having a ...
0
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0answers
28 views
Calibration curve motivation
I struggle to understand the mathematical motivation for the binary classification model calibration curve.
Why do we assume that the predicted probabilities should be consistent with the proportion ...
4
votes
1answer
24 views
Does the Bayesian MAP give a probability distribution over unseen data?
I'm working my way through the Bayesian world. So far I've understood that the MLE or the MPA are point estimates, therefore ...
0
votes
0answers
27 views
Bayes Classification Problem
Let we have 2 classes with their densties :
p(x| w1)=$$\tfrac12e^{-|x|} $$
P(x|w2) = $$e^{-2|x-2|}$$
Where x is a one dimension feature vector .
Now it is required to be known where to place the ...
1
vote
0answers
21 views
Algorithm to determine a single output value based on multiple input values [closed]
The main challenge is the lack of data.
Input values come from tests results of patients. A patient takes a breath test at an interval during a timespan. The result values can range from 0 to ~200, ...
1
vote
1answer
32 views
Combining multiple probabilities from a classifier. Propagating probabilities
Let's say I have trained a classifier that classifies images of animals into 10 different classes. And let's say that I have 20 different images of a particular animal and because I know the ...
0
votes
1answer
90 views
What is the num_initial_points argument for Bayesian Optimization with Keras Tuner?
I've implemented the following code to run Keras-Tuner with Bayesian Optimization:
...
0
votes
1answer
58 views
Hyperparameter tuning with Bayesian-Optimization
I'm using LightGBM for the regression problem and here is my code.
...
6
votes
1answer
380 views
Changing the batch size during training
The choice of batch size is in some sense the measure of stochasticity :
On one hand, smaller batch sizes make the gradient descent more stochastic, the SGD can deviate significantly from the exact ...
4
votes
1answer
80 views
How is bayesian risk computed to prune decision trees?
I've been trying to follow this paper on Bayesian Risk Pruning. I'm not very familiar with this type of pruning, but I'm wondering a few things:
(1) The paper describes risk-rates to be defined per ...
1
vote
1answer
50 views
Visualize n-dimensional bayesian optimization results
I am working on a 6-dimensional bayesian optimization problem using (skopt's gp_minimize).
After the optimizer ran for j iterations I would like to somehow visualize the "progress/result" of ...
3
votes
1answer
124 views
Why do machine learning engineers insist on training with more data than validation set?
Among my colleagues I have noticed a curious insistence on training with, say, 70% or 80% of data and validating on the remainder. The reason it is curious to me is the lack of any theoretical ...
0
votes
0answers
21 views
I think a learning rate schedule would be counter-productive with AdaBelief. Am I wrong?
I am inclined to believe the concept of a learning rate schedule is overcome by the improvements of Adabelief over Adam.
My code is on Github; please check it out and attempt to replicate my results, ...
1
vote
0answers
9 views
Tracking time-series latency using conjugate priors
I need to do a project using Bayesian statistics for a class and I am trying to apply it to my work.
I help manage a time series database with 40,000+ different time series that we collect. The time ...
0
votes
2answers
38 views
Books about statistical inference [closed]
I'm currently taking a course "Introduction to Machine Learning" which covers the following topics:
linear regression, overfitting, classification problems, parametric & non-parametric ...
0
votes
0answers
43 views
Practical difference between Bayesian Neural Networks and Feed Forward Neural Network with Gaussian Noise
To my understanding, a BNN's weights come from a Gaussian with trained mean and standard deviation, while a FFNN of the following form, comes from a learned weight, which acts as a 'mean', and is ...
0
votes
0answers
33 views
Bayesian Regression Model
I am new to Bayesian modeling. I am running Bayesian regression model in R using brm function from brms library, which is powered by STAN. I have a data with 10 million records. I took 10% sample out ...
2
votes
1answer
114 views
What is the difference between maximum likelihood hypothesis and maximum a posteriori hypothesis?
I am a student and I am studying machine learning. I am focusing on the concept of Bayesian learning and I have studied the maximum likelihood hypothesis and the maximum a posteriori hypothesis.
I ...
1
vote
1answer
99 views
Problem understanding probabilistic generative models for classification
I am a student and I am studying machine learning. I am focusing on probabilistic generative models for classification and I am having some troubles understanding this topic.
In the slide of my ...
1
vote
0answers
45 views
Poisson model with overdisperssion
I'm working with a dataset $X$ (of length $N$) of count data, which looks like:
I developed a statistical model which can be improved, so I'm asking for any suggestions, for instance, differnet ...
0
votes
1answer
152 views
What makes the posterior intractable?
In the setting of Variational AutoEncoders, i.e. when we want to find the posterior distribution
over the data generating, latent variable z, given some ...
3
votes
1answer
99 views
When to use bayesian linear regression instead of linear regression?
When does it make sense to use a bayesian approach, maybe in context to linear regression?
To be more concrete: Assume you measure a certain number of devices and you wanna' check the linear ...
1
vote
0answers
39 views
Custom Loss Function for Mixing Sparse and Dense Features for a Prediction Problem
I have a largely uncorrelated feature space of about 40 dichotomous features, using which I'm trying to predict a continuous target variable.
Now, some of these features are very sparse (Active less ...
1
vote
0answers
22 views
Improve confidence interval accuracy
I am doing a linear regression on log-transformed data and I use the bayesian approach to model the predictive distribution and construct my 90% prediction Interval.
The problem with this approach is ...
0
votes
1answer
128 views
BPR TripletLoss Recommender System
I am trying to modify the code of this repo to build a recommender system based on BPR triplet loss.
In particular I modified the TripletLoss layer class like this
...
4
votes
2answers
155 views
Mean estimation for nested location data
I want to estimate the average income for a location. I have nested data in the following way: A block is inside a neighborhood, which is inside a zipcode, which is inside a district, which is inside ...
1
vote
2answers
2k views
Opinions on an LSTM hyper-parameter tuning process I am using
I am training an LSTM to predict a price chart. I am using Bayesian optimization to speed things slightly since I have a large number of hyperparameters and only my CPU as a resource.
Making 100 ...
0
votes
0answers
65 views
Classification of OLS regression coefficients
A variable $A$ (reaction time) is log-normally distributed, i.e. $\log(A) \sim \mathcal{N}(0,\sigma^2)$ and is linearly dependent of $n$ variables $X = (X_1,\ldots,X_n)$, i.e.
\begin{align}
A &= \...
1
vote
0answers
81 views
Firebase AB testing algorithm
We have run an AB test at firebase which has the following results:
I was also building my own Bayesian AB-test suite and was wondering how they came to these conclusions.
What I was doing was ...
0
votes
1answer
42 views
Hyperparameter tuning of neural networks using Bayesian Optimization
One of the assumptions for finding good hyperparameters using Bayesian optimization (GP) is that the unknown function is smooth. Is this assumption valid for neural networks or at least for most of ...
1
vote
0answers
39 views
Specifying priors in rstanarm for hierarchical model
We are given the model $$
\begin{align*}
y_{ij} & \sim \mathsf{Normal}(\alpha_j + \beta x_i, \sigma^2)\\
\alpha_j & \sim \mathsf{Normal}(\gamma_0 + \gamma_1 u_j, \tau^2)
\end{align*}
$$ with ...
4
votes
1answer
58 views
Bayesian regularization vs dropout for basic ann
Does it make sense conceptually to apply dropout to an artificial neutral network while also applying bayesian regularization?
On one hand I would think that technically this should work just fine, ...
2
votes
1answer
27 views
How to calculate probability of non independence using bayes theorem?
i looked into one of the post about naive bayes calulation of naive part
Predit the class label for instance (A=1,B=2,C=2) using naive Bayes classifcation.
Let C1 be class 1 and C2 be class 2.
For ...
2
votes
1answer
78 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 ...
3
votes
1answer
37 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 ...
3
votes
2answers
835 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
vote
0answers
31 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
votes
3answers
348 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
votes
1answer
247 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 ...
2
votes
0answers
109 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
votes
1answer
84 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 $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
2
votes
1answer
29 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 ...
1
vote
0answers
27 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 ...
