Questions tagged [bayesian]

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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 &= \...
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82 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 ...
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1answer
52 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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1answer
1k 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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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 ...
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1answer
111 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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1answer
64 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, ...
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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 ...
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1answer
80 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 ...
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2answers
2k 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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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) =...
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3answers
359 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)}{...
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1answer
253 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 ...
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110 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 ...
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2answers
122 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 ...
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1answer
178 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 ...
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94 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, ...
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1answer
22 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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1k 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
251 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 ...
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1answer
102 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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34 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 ...
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1answer
371 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
33 views
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2answers
883 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
47 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 ...
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2answers
262 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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71 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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2answers
5k 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
65 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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1answer
83 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
1k 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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45 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 ...
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25 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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718 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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1answer
2k views

Modeling uncertainty from Logistic Regression

Logistic regression is a part in a simulation pipeline that I use for some scenario analysis. The dataset that this is based on is not small but relatively noisy, and only one explanatory variable/...
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1answer
991 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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27 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 ...
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1answer
1k views

Why the estimated Lasso coefficients of almost all variables are equal to zero?

I would greatly appreciate if you could guide me. In fact, I used "Bayesian Optimization " to tune hyper-parameters of Lasso but the estimated Lasso coefficients of almost all variables are equal to ...
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399 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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69 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 ...
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1answer
74 views

ML algorithm where variable importance depends upon other variables - specifying conditionality

I am designing an algorithm that can detect cheating in a game of chess. I have a database of players who have been flagged by moderators as cheating, along with a number of game-level ...
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1answer
427 views

Replacing missing value by class conditional mean

I have two classes, $p(x|y=0)$ and $p(x|y=1)$ with ${{\mu }_{0}}$ and ${{\mu }_{1}}$ as mean and shared covariance matrix $\Sigma $. Now, I have a missing feature ${{x}_{n}}$ for a particular ...
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200 views

How to use pymc3 to sample the mean of a Pareto random variable?

I Have a variable which is Pareto-ly distributed 'x', with unknown alpha and m. I want to find out the distribution of its mean, so I use the following model: ...
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56 views

information leakage when using empirical Bayesian to generate a predictor

Consider the following problem: I want to predict the next bat of a set of baseball player. I have a training data set, where it contains the historical bat records (0-1 encoded, which is our target ...
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2answers
336 views

Ensemble learning [closed]

I am currently working to build a mathematical model to predict the stock market. I learned that the best way to do such thing is no longer to make one big best model, but rather to gather several ...
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2answers
2k views

generalized likelihood ratio test (GLRT)

I am having trouble in understanding the generalized likelihood ratio test (GLRT). Can anyone explain what it is to me, or point me toward an easy-to-understand reference? Is it a supervised or ...
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0answers
480 views

Implement gaussian mixture model with stochastic variational inference

I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper. This is the pgm of Gaussian Mixture. According to the paper, the full algorithm of ...
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1answer
36 views

Finding the most important questions from a questionary-> results

Lets assume that I have 5000 articles and I create the TF-IDF of these articles. Now I ask som people to answer 30 questions and I create the TF-IDF of these answers from the IDF of the articles and ...
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1answer
104 views

Is deep learning a must in a Data Science MSc programme? [closed]

I am reading the programme outline of this two-year MSc in Data Science and I found that it has no deep learning content (as in many other european ones). I am no expert but as far as I've seen I ...