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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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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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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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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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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3
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1
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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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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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2
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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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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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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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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
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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 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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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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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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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 ...
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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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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 ...
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probability distribution
Just wanted to know if the value we get by passing, say, random.normal(shape=(3,2)) in the Tensorflow, etc, are normally distributed or if they are randomly chosen ...
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Calculationg perplexity (in natural language processing) manually
I am trying to understand Perplexity within Natural Language Processing as a metric more fully. And I am doing so by creating manual examples to understand all the component parts. Is the following ...
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Probability for Nth Place in Race from Bradley-Terry Model Inputs and Outputs
I have created a motorcycle race prediction model that is given pairs of racers and outputs the probability of each rider beating the other in each pairwise comparison. That info is then processed ...
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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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0
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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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251
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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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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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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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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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203
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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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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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How can I interpret my rho risk values when performing probabilistic time series forecasting?
I am currently exploring different probabilistic time series forecasting models for car sales data and have planned to evaluate the probabilistic forecasts with the metrics rho-risk as described on ...
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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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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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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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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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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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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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2
answers
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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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Update of mean and variance of weights
I'm trying to understand the Bayes by Backprop algorithm from the paper Weight Uncertainty in Neural Networks, the idea is to make a NN in which each weight has it's own probability distribution. I ...
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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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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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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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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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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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