# Questions tagged [probability]

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur or how likely it is that a proposition is true.

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### Loss Function for Probability Regression

I'm trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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### Relation between an underlying function and the underlying probability distribition function of data

I heard and read a lot of times the following statements and got a lot of confusion over time. Statement 1: The goal of machine learning is to get a function from the given data Statement 2: The goal ...
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### Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...
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### How to make machine learning model that reports ambiguity of the input?

Suppose I want to build a neural network regression model that takes one input and return one output. Here's the training data: ...
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### Marginalization of joint distribution

I am trying to understand how you marginalise a joint distribution. In my case I have a fair coin, $P(C) = \frac12$ and a fair dice $P(D) = \frac16$. I am told I win a prize if I flip the coin and ...
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### What algorithms can handle probabilistic targets?

I have a classification problem where I want to want to use probabilities instead of classes to train my model to learn to output probabilities. In my dataset, I have instances where the probabilities ...
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### why does my calibration curve for platts and isotonic have less points than my uncalibrated model?

i train a model using grid search then i use the best parameters from this to define my chosen model. ...
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### how can i plot probability distribution of my classes in the way below?

All, I would like to plot the following: I have a binary classification problem where I am using xgboost as my 'model' below: ...
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### Notation for features (general notation for continuous and discrete random variables)

I'm looking for the right notation for features from different types. Let us say that my samples as $m$ features that can be modeled with $X_1,...,X_m$. The features Don't share the same distribution (...
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### Practical example and working of Laplace Smoothing or Linear Interpolation in Natural Language Processing (NLP)

Let us suppose we have a document where total_words = 50 (for example -> is,the,now,is,am,here,now) total_unique_words = 40 (for example -> is,the,am,here,now) How can we apply the ...
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### overlapping of surfaces with holes

I have two disks, of equal diameter. Both disks have a 'spatter' of holes, in random positions (from a two-dimensional - i.e., surface - uniform distribution; in other words, holes close to the sides ...
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### Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
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### Difference between Gibbs sampling and variational Bayes inference

After reading in blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. Below link described but it's difficult to ...
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### What is the difference between KL-divergence, JS-divergence, Wasserstein distance and MMD?

I was reading about different distribution distances, and came across Kullback-Leibler divergence Jensen-Shannon divergence Wasserstein distance Maximum mean discrepancy (MMD) The book was too ...
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### Wavenet joint probability

As presented in the first article of Google Wavenet (https://arxiv.org/pdf/1609.03499.pdf) the model can approximate the joint probability of the whole sequence (raw audio waveform) using the chain ...
4k views

### Calculating an estimate of KL Divergence using the samples drawn from distributions

Given two sets of samples drawn from two different distributions, is it computationally possible to get an estimate of KL-Divergence between the two distribution using these samples? Here I am ...
108 views

### Store's unseen items sales forecasting

I am working on sales forecasting problem.I am able to provide data about which items got sold and not sold to the algorithm.How to provide algorithm information about items that are not present in ...
180 views

### Epoch greedy algorithm for contextual bandits

I'm reading the following paper on the epoch greedy algorithm for the contextual bandits problem. I have two questions http://hunch.net/~jl/projects/interactive/sidebandits/bandit.pdf I'm unsure ...
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### Do I need to use Bayes to combine a sample's class probability with the performance of the overall model?

For a classification algorithm that gives the predicted probabilities for each class (ie random forest in sklearn), by default the classes are separated with a score of 0.5. After evaluating the ...
159 views

### Modeling the influence of events order on probability

The case is to model if the sequence of events influences the probability of binary target variable. We have for example five different events which occur in time (event: A,B,C,D,E). They can occur in ...
476 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 ...
37 views

### Is there a counting sketch optimized for intersections?

Popular counting sketches(loglog, hyperloglog, etc) feature natural union operations. Are there any known counting sketches that feature natural intersection operations?
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### predicting probability distribution for time series

I have time series of several variables. Just in one specific case one variable is linear combination of the rest. I want to predict probability distribution (that is not only best estimate but ...
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### Predicting missing data. Looking for good data predicting technique

I am analysing data for Countries Trade GDP. Some of the countries have missing GDP value for given a year. However, I have Grand Total for the entire region for that year. Is there a good data ...
37 views

### How to derive Evidence Lower Bound in the paper “Zero-Shot Text-to-Image Generation”?

Can someone share the derivation of Evidence Lower Bound in this paper ? Zero-Shot Text-to-Image Generation The overall procedure can be viewed as maximizing the evidence lower bound (ELB) (Kingma &...
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### Use distribution probability as a feature in ML model

I built an LSMT model to predict sick cows. I also have risk factors like cow size and height (static risk factor) that I want to combine into the ML model. I found that size is geometrically ...
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### 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 ...
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### Ensemble of different reservoirs (echo state networks)

Suppose I want to do reservoir computing to classify the input to the proper category (e.g. recognizing a handwritten letter). Ideally, after training a single reservoir and testing it, there would be ...
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### different scored probability distributions for a classification model using python vs Azure ML Studio

I have a classification model which I initially built in Azure ML studio and then created a similar model in python (its similar because the algorithms in python VS Azure are a bit different so they ...
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### How to predict when an appointment will be scheduled?

I have a dataset of tens of thousands of appointments. Appointments have a created date and scheduled date. Something like this: ...
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### How to understand Bionomial Theroem and the Recursion Rule?

In this video from EDX, the instructor explains the binomial theorem as: Binomial Theorem: When you calculate $(a + b)^n = a^n + C(1)a^{(n-1)b} + C(2)a^{(n-2)b^2} + ... + C(n-1)ab^{(n-1) + b^n}$ The ...
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### What probability distribution would be more appropriate for monthly rate of going to the store?

Part of a model I am making includes the frequency with which people go shopping for a given good (e.g. people on average go to the supermarket some n times a month). I am trying to figure out what ...
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### Probability of Gaussian Naive Bayes

How would I go about attaching a probability to the prediction outputted by a Gaussian Naive Bayes model ? I'm asking because the predict_proba function U can use with sklearn's Gaussian Naive Bayes ...
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### How is the standard deviation of VAE's obtained?

I am trying to build a Variational Autoencoder. I was looking at various codes online and found most of them in some way or another copy Francois Chollet (Google researchers) code. Now my main ...
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### Very low probability in naive Bayes classifier 1

I have some training data (TRAIN) and some test data (TEST). Each row of each table contains an observed class (X) and some columns of binary (Y). I'm using a Python script that is intended to predict ...
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### Probabilistic Machine Learning model to match spatial data

I have spatial data from multiple sources. This data consists of ID, lat, long, and time. My goal is that given a new lat-long, the model needs to return (preferably with a probability) the data ...
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### How to elegantly caclulate probability distribution parameters for a particular random variable given some observed data?

I am using bnlearn package and R to learn Bayesian Network structure and also fit it using Maximum Likelihood estimation(MLE). bn.fit uses MLE to learn[as i understand] a generalized probability ...
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### Probability Distribution of the Process Duration Time based on the Process Status Measurements

I have data of execution of several processes. Each execution is identified by the process_id. The duration of the execution is measured by external agents, testing ...