Questions tagged [bayesian]
The bayesian tag has no usage guidance.
89
questions
3
votes
1answer
71 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
24 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
56 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
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0answers
16 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
27 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
14 views
Hypothesis Space of Bayes' Optimal Classifier?
What is the hypothesis space for an Optimal Bayes' Classifier and why do the assumptions of a Naive Bayes' Classifier (that features are conditionally independent of each other) narrow the search ...
0
votes
0answers
36 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
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0answers
28 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 ...
1
vote
1answer
53 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
88 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
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0answers
44 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
39 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
77 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
29 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
19 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
0answers
10 views
Machine learning. Input: set of distributions, Output: distribution
I have a set of features where typical machine learning techniques do not work very good. All features have very different distributions, some heteroscedasticity is also present.
The distribution ...
0
votes
1answer
76 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
870 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
45 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
62 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
37 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 ...
0
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0answers
11 views
what does “Tree” refer to in Tree-structured Parzen Estimators
I am going through the literature of Hyperparameter optimization techniques and came across TPE. There is very little to no explanation on why the name has "Tree" in it. What is Tree referring to? and ...
0
votes
0answers
35 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
42 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
18 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
73 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
votes
0answers
29 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 ...
3
votes
0answers
31 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
393 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
29 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
304 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
189 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 ...
1
vote
0answers
18 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
votes
0answers
106 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
81 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
24 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
votes
0answers
25 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
83 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
votes
1answer
15 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 ...
1
vote
2answers
70 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
votes
1answer
126 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
votes
1answer
77 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 ...
2
votes
1answer
93 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. ...
1
vote
0answers
957 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) ...
2
votes
1answer
153 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 ...
3
votes
1answer
907 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 ...
1
vote
0answers
31 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
votes
2answers
730 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 ...
