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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1answer
105 views

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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31 views

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 ...
3
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0answers
43 views

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: ...
3
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1answer
38 views

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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0answers
16 views

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 ...
2
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1answer
84 views

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. ...
2
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0answers
78 views

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: ...
2
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2answers
64 views

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 (...
2
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0answers
24 views

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 ...
2
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0answers
30 views

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 ...
2
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0answers
15 views

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 ...
2
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53 views

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 ...
2
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0answers
171 views

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 ...
2
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0answers
35 views

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 ...
2
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2answers
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 ...
2
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1answer
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 ...
2
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0answers
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 ...
2
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0answers
22 views

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 ...
2
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2answers
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 ...
2
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0answers
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 ...
2
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0answers
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?
2
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1answer
1k views

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 ...
2
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2answers
144 views

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 ...
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0answers
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 &...
1
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1answer
25 views

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 ...
1
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1answer
28 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 ...
1
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0answers
8 views

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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0answers
10 views

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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0answers
6 views

Error term in probabilistic interpretation of least squares update rule

I have read in Stanford's CS229 course notes that to justify the least-squares update rule with probability, the following is assumed: $$y^{(i)} = \theta^Tx^{(i)}+\epsilon^{(i)}$$ , where $\epsilon^{(...
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0answers
12 views

Learn smoothly varying mean and variance of a variable over a 2d domain

For a problem which I am working on at the moment, I'm interested in learning how the mean and variance of some response variable y changes with two independent ...
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2answers
39 views

How to generate a random sample and distribute values based in an probability distribution?

I want to generate a random sample based on this probability distribution: The line is the KDE of the histogram. My random sample will have n values, the value is ...
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0answers
26 views

Separation of two probabilistic distribution: gaussian and exponential

I have a dataset where I highly suspect some correlation by two variables, based on my understanding of the problem. I would have a linear correlation (sort of SVD decomposition or a plane such as $y=...
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2answers
84 views

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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1answer
37 views

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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1answer
27 views

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 ...
1
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1answer
77 views

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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0answers
38 views

Ranking graph's nodes by score propagation

Problem I have the following directed tripartite graph $G(E\cup V\cup P, A)$, where there is a many-to-one symmetric relationship between the subsets V and E - $e\in E,v\in V,[e, v]\in A \iff [v, e]\...
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0answers
36 views

“Smearing” probabilities or how to handle imprecise locations for canonically classification-type problems

I am trying to predict failures at different nodes on a line. Each node has different weather features and hardware/configuration features. For a little under half of the historical failures I have, I ...
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0answers
18 views

How does one use activation function with greater than [-1;1] range for binary classification?

In Efficient Backprop (http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf), Lecun and others propose to use activation function that don't reach target values on their asypmptotes. They explain (§ 4....
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0answers
41 views

Calculate marginal probability distributions of a dataset

I have a dataset with 'n' features and a label(binary) corresponding each entry. I am using a predicitive model to predict these labels using those 'n' features. Now, I wish to know the marginal ...
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0answers
18 views

Is using cross-entropy enough to ensure the output is a distribution probability?

I am following along https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html. In this code, the last layers of the pretrained networks are linear. The loss used in this ...
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0answers
23 views

F-test for comparing the mean of two groups

What would be the procedure, in terms of F-test, if I would like to check if the mean of one group is greater than the other group (alternative hypothesis: $\mu_1 > \mu_2$)? And also what would ...
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0answers
60 views

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 ...
1
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1answer
166 views

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 ...
1
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1answer
77 views

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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0answers
17 views

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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0answers
62 views

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 ...
1
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3answers
1k views

Non-mutually exclusive classification sum of probabilities

So I have the following problem: I realized (while writing my master thesis) that I am still not sure/have vague descriptions of some of the machine learning principles. I already asked one question ...
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0answers
13 views

Which features of a data set can be used for market campaigning using propensity scores?

A dataset contains so many fields in which there is both relevant and irrelevant field. If we want to do a market campaigning using propensity scoring, which fields of the data set are relevant? How ...