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 am 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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4 votes
1 answer
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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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Analysis of probability distribution of each features and Machine Learning

While I know that probability distributions are for hypothesis testing, confidence level constructions, etc. They definitely have many roles in statistical analysis. However, it is not obvious to me ...
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  • 359
3 votes
1 answer
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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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3 votes
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45 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: ...
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  • 305
3 votes
1 answer
50 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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2 votes
1 answer
34 views

Threshold tuning with one-vs-rest for multi classification python

I’m currently using a One vs Rest Random forest algorithm for multi class classification problem using Python, and I want to find the optimal threshold for each class, How can I do this with OVR (One-...
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  • 21
2 votes
1 answer
28 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 ...
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  • 139
2 votes
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270 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: ...
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  • 466
2 votes
2 answers
149 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 (...
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2 votes
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31 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 ...
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2 votes
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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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2 votes
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108 views

Difference between Gibbs sampling and variational Bayes inference

After reading blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. The below link is described but it's difficult to ...
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2 votes
0 answers
293 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 ...
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2 votes
0 answers
38 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 ...
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2 votes
1 answer
122 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 ...
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2 votes
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190 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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2 votes
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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 ...
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2 votes
2 answers
181 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 ...
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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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2 votes
0 answers
39 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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  • 121
2 votes
1 answer
2k 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 ...
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2 votes
2 answers
148 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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1 vote
0 answers
15 views

Inject external prior distribution to my dataset

Input: External Information - distribution between the feature_i & binary_target Internal Dataset - tabular data. ...
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1 vote
0 answers
24 views

Best metric to evaluate model probabilities

i'm trying to create ML model for binary classification problem with balanced dataset and i care mostly about probabilities. I was trying to search web and i find only advices to use AUC or logloss ...
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1 vote
1 answer
73 views

Log odds vs Log probability

Log-odds has a linear relationship with the independent variables, which is why log-odds equals a linear equation. What about log of probability? How is it related to the independent variables? Is ...
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  • 263
1 vote
2 answers
71 views

Odds vs Likelihood

Odds is the chance of an event occurring against the event not occurring. Likelihood is the probability of a set of parameters being supported by the data in hand. In logistic regression, we use log ...
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  • 263
1 vote
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How are the weights defined in a (linear-chain) Conditional Random Field?

Edit: i saw that i mixed up i (in the graph) and t (in the formula), in the following i equivalent to t I am trying to understand the theory behind linear chain Conditional Random Fields. I have now ...
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1 vote
1 answer
32 views

How to test likelihood hypothesis on dataset?

How to test the following hypothesis? The larger the fare the more likely the customer is to be travailing alone. Using the data below, how would one be able to test the hypothesis? ...
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1 vote
1 answer
17 views

memory error- python N-th order Markovian transition matrix from a given sequence

Ok. What is wrong with you code! I am trying to calculate transition probabilities for each leg. The code works for small array but for the actual dataset I got memory error. I have 64 g version ...
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1 vote
1 answer
55 views

Probability of the occurrence of an event over time

I need to answer the following question: What is the probability that the event 1 will occur at some point in the time for a new sample? At which time point is this more likely to occur? 1: event ...
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1 vote
0 answers
19 views

Predictions using calibrated classifer

I find myself asking alot of calibration related questions recently - but i cannot find adequate material on it! I am training a binary classifier to predict default. This probability will be used in ...
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  • 466
1 vote
0 answers
23 views

Shannon Information Content related to Uncertainty?

I'm a data scientist student currently writing my master thesis which resolves around the Cross Entropy (CE) Loss Function for neural networks. From my understanding, the CE is based on the Entropy, ...
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1 vote
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27 views

Train a parametrized model to sample from a known target distribution

I wonder if there is a way to train a parametrized model to sample from a known distribution such as Gaussian. We usually don't need a model to sample from a known distribution (if we know the CDF for ...
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1 vote
0 answers
47 views

Predicting probabilities in Neural Networks

I have 1000 number of inputs in a sample each ranging between 0-1 as shown: ...
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1 vote
0 answers
27 views

Deep Learning book - trying to understand Bernoulli formulas

In the section 6.2.2.2 Sigmoid Units for Bernoulli Output Distributions of The Deep Learning Book there is a section: (z is defined as $z=w^Th+b$ and $\hat{y}=\...
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1 vote
1 answer
54 views

How to compare Poisson Point Process, ARIMA and LSTM?

I am trying to compare three forecasting techniques: A stationary stochastic Poisson-GEV: where the rate of occurrence of the events is given by a Poisson process and, it's intensity is given by a ...
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1 vote
0 answers
43 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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  • 11
1 vote
2 answers
117 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 ...
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  • 11
1 vote
1 answer
41 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 ...
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1 vote
0 answers
20 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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1 vote
0 answers
14 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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1 vote
0 answers
13 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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1 vote
0 answers
18 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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1 vote
3 answers
147 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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1 vote
1 answer
141 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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1 vote
0 answers
67 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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  • 111
1 vote
0 answers
41 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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1 vote
0 answers
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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1 vote
0 answers
60 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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