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Questions tagged [probability]

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create joint prob distribution or empirical relation for two variables

There are two variables, X1 and X2. The experimental study shows that they are highly correlated. Are there any reliable ways to create an empirical mapping(or equation) between X1 and X2. Assuming ...
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5 views

Confidence vs. Count in association rule mining: which one is better?

I am writing a program that mines association rules from a large data set. I have an array of association rules, and I have to decide which ones are more representative of the patterns I am studying. ...
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15 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 ...
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1answer
21 views

Predict how many days late or early someone will finish their work

So I have a set of deadlines and people, with a database of when those people finished their previous work and how much after the deadline it was, as well as when the work was given. The work itself ...
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21 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 ...
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20 views

Probabilistic guarantees for SQL query execution

We are working on a project that tries to accelerate Spark SQL using dedicated hardware. Our approach works much better (in terms of the resulting latency) if we allow query processing that is only ...
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0answers
38 views

Various Distances between probability distributions

I was reading about different distribution distance - and came across Kullback-Leibler divergence, Jensen-Shannon divergence, MMD, and Wasserstein distance - the book was too abstract for me to absorb....
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10 views

Poisson point process application and terminology

I am trying to understand Poisson process that is used in a derivation of maximum likelihood estimation of intrinsic dimension given here https://wiki.math.uwaterloo.ca/statwiki/index.php?title=...
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1answer
32 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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2answers
56 views

Why does a belief network need to be represented using a directed acyclic graph (DAG)?

I would have thought that it's because DAGs preserve the dependency relationships between the variables, but I am currently unsure. Thanks
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2answers
51 views

Why do people use CrossEntropyLoss and not just a softmax probability as the loss?

I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1....
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9 views

the joint distribution for graphical models with circles

For the following graphical model, what should be the joint distribution of P(X1,X2,X3,X4). Comparing with normal Bayesian network, x2 and X3 are correlated as well.
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3answers
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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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26 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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1answer
21 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
8 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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1answer
37 views

Using classifier's probabilities as independent variables to predict Y

Suppose you have a classification task y~X with (n_samples,m_features). A colleague told me that it is correct to run r different classifiers to predict y based on X and then use the probabilities ...
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0answers
16 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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1answer
20 views

Layman's description of PDF and CDF [closed]

Can anyone please explain what a PDF and CDF are in simple words. (Please don't define it from wiki.)
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0answers
21 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 ...
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0answers
25 views

xgboost or lightgbm to handle Binomial problems

I have a dataset containing a column of trials, a column of successes and other features; and, obviously, I can generate a probability column. I would like to use gradient boosting methods (like ...
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1answer
61 views

How to get probabilities values with keras?

tensorflow version = '1.12.0' keras version = '2.1.6-tf' I'm using keras with tensorflow backend. I want to get the probabilities values of the prediction. I want the probabilities to sum up to 1. ...
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0answers
22 views

How to optimize function built on top of the classifier?

I have a dataset with classification model build for it for $n$ classes as target. And also using the probabilities for each class, which classificator returns, I built confidence function for each ...
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1answer
12 views

the probability distribution of dependent variables

There are three variables, X3 is a function of X1 and X2, ...
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1answer
65 views

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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0answers
14 views

How to use SLAM on other sensor other than camera?

I have a sensor that reads electromagnetic field strength from each position. And the field is stable and unique for each position. So the reading is simply a function of the position like this: <...
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0answers
8 views

To what extent can one link data in a structured or rigorous way, given only partial knowledge base?

I seem to have stumbled on an interesting problem: suppose I am given partial information in two tables, say imports and exports between different countries, and I can query these, but e.g. the ...
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0answers
8 views

How to determine the rate of entry in a queue M/M/c?

I have the exit rate ($\mu$) and the average waiting time in the queue ($W_q$). I need solve to rate of input ($\lambda$) in a queue. I now: $\rho = \frac{\lambda}{c\mu} < 1$ $\pi_0 = \left[\left(...
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0answers
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sample n unique items from dataset

I have a dataset that has N of different unique items and each item appears Ai times (every item appears different times). This is mean that I have the ...
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0answers
13 views

How do I combine two electromagnetic readings to predict the position of a sensor?

I have an electromagnetic sensor and electromagnetic field emitter. The sensor will read power from the emitter. I want to predict the position of the sensor using the reading. Let me simplify the ...
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2answers
43 views

Trying to find the correlation between inputs and output

I have tried the pandas code for trying to find out the correlation between the output and the inputs I am feeding. Here is the code: ...
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0answers
12 views

Probabilistic search engine user behavior markov models

I am considering the following probabilistic Markov model of actions of a user on the results page of a search engine. The user examines the first result, with a probability $A$ he is satisfied with ...
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2answers
38 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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1answer
66 views

Calibrate the predicted class probability to make it represent a true probability?

Let's say that we have a simple binary classification model (a neural network -- NN) for classifying input images as "dog" ($y=1$) or "not dog" ($y=0$). Let's assume that the NN has one "sigmoid ...
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1answer
21 views

Historical weather data with machine learning? [closed]

My company gave me a task to build some weather forecasting. I have now historical weather data for 10 years (temperature, precipitation in mm, humidity and etc. more than 30 features total). We need ...
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0answers
32 views

Do I need to correct predict_proba by training fraction?

Many algorithms provide a predict_proba function indicating probability of a case to belong to that class (e.g. https://scikit-learn.org/stable/modules/generated/...
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1answer
25 views

How to predict unknown(hidden) value by incomplete value or partly recorded value

Let me make it clear by make an example: Suppose I knew a person's cost each month for 3 years like: 2016Jan : $2500 2016Feb : $4000 2016Mar : $3500 ... Just according to this, can I ...
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0answers
26 views

Comparing two sets of exponential data T(t)=−e^(−kt)

I have two sets of exponential data (temperature measurements) of the form: $T(t)=−e^{(−kt)}$. $k$ is a constant that determines the rate of temperature change. 1 temperature measurement was taken ...
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1answer
25 views

Extending DTW 1-NN Classification to On-line Scenario

I am familiar with Dynamic Time Warping classification using a 1-nearest neighbour approach. However, in most benchmark datasets and applications, it used ex-post, i.e. classifying a time series after ...
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1answer
32 views

objective in policy gradient equation?

I don't understand how this was deduced from first equation to second expectation. Is it from conditional probability theory? I checked but still can't understand. From wikipedia, the expectation of a ...
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2answers
64 views

Maximum likelihood estimation vs calculating distribution parameters “manually”

I'm sorry for asking probably elementary question, but I cannot understand how estimating probability distribution parameters using maximum likelihood estimation method differs from calculating these ...
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1answer
25 views

Statistical machine translation word alignment for FR-ENG and ENG-FR: what is p(e) and p(f)?

I'm currently trying to implement this paper, but am struggling to understand some of the math here. I'm pretty sure I understand how to implement the E-step, but for the M-step, I'm confused on how ...
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1answer
69 views

Capturing movement importance - logistic regression output

I'm studying some event for a set of objects that can be plotted on a square $[0, 100] ^ 2$. I have used logistic regression to calculate probabilities that event occur for different objects and the ...
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1answer
84 views

Can someone please explain what this sample function is upto?

So there is a function in Dino_Name_Generator at Deeplearning.ai notebook ...
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3answers
585 views

Why does the naive bayes algorithm make the naive assumption that features are independent to each other?

Naive Bayes is called naive because it makes the naive assumption that features have zero correlation with each other. They are independent of each other. Why does ...
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1answer
36 views

mathematical accurate definition of the binary independence model

I have a hard time understanding the exact mathematical meaning behind the binary independence model. On wikipedia we can see the following definition or similarly in the book from Manning and ...
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1answer
60 views

Help solving Bigram Model with the following probabilities

I came across the following problem involving bigram models which I am struggling to solve. Following this tutorial I have a basic understanding of how bigram possibilities are calculated. Problem: ...
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1answer
298 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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3answers
1k views

How to convert an array of numbers into probability values?

I would like some help with respect to certain numerical computation. I have certain arrays which look like: Array 1: [0.81893085, 0.54768653, 0.14973508] Array 2: [0.48078357, 0.92219683, 1.02359911]...
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41 views

Time Series Autocorrelation Estimation

How do I find joint PDF f(xi,xj) needed for estimating autocorrelation from 1-D time series data X, using the kernel windowing technique? I am well aware of kernel windowing technique for estimation ...