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Questions tagged [probability]

The tag has no usage guidance.

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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 ...
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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 ...
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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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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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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
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the probability distribution of dependent variables

There are three variables, X3 is a function of X1 and X2, ...
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1answer
38 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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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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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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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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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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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
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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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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
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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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Why the outputs of a machine learning model are not sampled at the prediction time?

Let's say there is a dataset D with input X and corresponding output y. Let's assume that <...
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1answer
38 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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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
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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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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
27 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
50 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
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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
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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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489 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
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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
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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
181 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
623 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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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 ...
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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 ...
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1answer
89 views

Poker tournament winner prediction

I am trying to solve poker tournament winner prediction problem. I’ve millions of historical records in this format: Players ==> Winner P1,P2,P4,P8 ==> P2 P4,P7,P6 ==> P4 P6,P3,P2,P1 ==> P1 I ...
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1answer
131 views

PMI between lemma vs surface

I was wondering whether it's possible to compute the some sort of pointwise mutual information between lemma and its surface form. First if we assume, ...
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1answer
28 views

Differentiating roadmap of a loss function

Let's say I'm performing Stochastic Gradient Descent (SGD) on binary cross entropy error while optimizing weight $w_{2}$. Binary cross entropy error: $$L(y|p(x_{i}))=-y_{i}*ln(p(x_{i}))-(1-y_{i})*...
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1answer
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How to generate a sample from a generative model like a Restricted Boltzmann Machine?

I am learning about the Boltzmann machine. So far, I have successfully written a code that can learn the coefficients of the energy function of a Restricted Boltzmann Machine. Now, since my model is ...
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Measuring minority class frequency (with confidence interval) with calibration data

I have a large amount of unlabeled data, a binary classifier that outputs a score (uncalibrated) and a small amount of labeled data that was used to create a calibration plot. When I run my ...
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1answer
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How does binary cross entropy work?

Let's say I'm trying to classify some data with logistic regression. Before passing the summed data to the logistic function (normalized in range $[0,1]$), weights must be optimized for desirable ...
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correcting conditional and marginal distribution in transfer learning

I understand that in case of transfer learning, we can have the target and the source data having different domain distributions. In such cases, authors in many papers suggest bringing the marginal ...
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1answer
21 views

Predicting object by features probabilities

I have the definition of an object provided as features probability. Each object has it's own feature importance and probabilities. For example for object "X", I have "color" feature (with the weight ...
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3answers
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Distance between very large discrete probability distributions

I have 192 countries where each country has some value for 1 million attributes which sum up to 1 (a discrete probability distribution). For any one country most of the values for the attributes are 0....
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Apparent Paradox between Principle of Maximum Entropy and Perplexity

As I found the principle of maximum entropy said: the probability distribution which best represents the current state of knowledge is the one with the largest entropy And the definition of ...
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1answer
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How can I estimate user-item purchase probabilities of a e-commerce website?

I am writing my Master thesis, where the goal is to estimate user-item purchase probabilities. In other words, for a given user, what is the probability he/she will buy a certain item. I have session ...
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How do we create the graph structure required for unimodal bandits

Some of the papers describing Unimodal bandits are [1], [2] and [3]. The common factor in all these algorithms is that they require a graph as input whose nodes are arms (actions) and an edge exists ...
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keras categorical CNN prediction always output [[ 0. 0. 0. 0. 0. 0. 1. 0.]] No probability for other classes?

print(model.predict(image)) print(model.predict_proba(image)) print(model.predict_classes(image)) Using the above different predict methods I always get ...
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1answer
52 views

Which machine learning model should I learn for this problem?

I'm working in python. Would like to practice some machine learning, and I've always been curious about an analog to the problem below... A collection of 3 letters are drawn randomly from the 26 ...
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Optimality of Bayes classifier

I've studied an introdution to the bayer optimal classifier and i've understood how it works. What isnt clear to me is why it's considered optimal, and what it means to be optimal in this case. Can ...
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Machine learning with stochastic labels

I'm sure this is a standard problem in machine learning so links to pages or books that discuss the problem are also welcome. The problem is: We have some input data points, $x_i$, and some output ...