Questions tagged [probability]
For question involving probability as related to AI methods. (This tag is for general usage. Feel free to utilize in conjunction with the "math" and more specific probability tags.)
38
questions
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0answers
14 views
How to translate machine probability output into visual representation
If a machine prediction score is built over a list of features that has a probability 0 to 1. Each of those features can be either positive or negative contributors. We’re visualization that in the UI ...
1
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0answers
18 views
Neural network seems to just figure out the probability of a specific result
I am really new to neural networks, so i was following along with a video series, created by '3blue1brown' on youtube. I created an implementation of the network he explained in c++. I am attempting ...
2
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0answers
43 views
Formulation of a Markov Decision Process Problem
Given a list of $N$ questions. If question $i$ is answered correctly (given probability $p_i$), we receive reward $R_i$; if not the quiz terminates. Find the optimal order of questions to maximize ...
0
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1answer
27 views
Predicting probabilities of events using neural networks
I've got a few thousands of sequences like
1.23, 2.15. 3.19, 4.30, 5.24, 6.22
where the numbers denote times on which an event happened (there's just a single ...
2
votes
1answer
42 views
How can supervised learning be viewed as a conditional probability of the labels given the inputs?
In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g.,
$\ \ \ \ \ \rho(\vec{y}|\vec{x},\vec{\theta})$
where $\vec{\theta}$ denotes a ...
2
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0answers
29 views
How can we prove this inequality, related to the generalization error, without using the Rademacher complexity?
This is an inequality on page 36 of the book Foundations of Machine Learning, but the author only states it without proof.
$$
\mathbb{P}\left[\left|R(h)-\widehat{R}_{S}(h)\right|>\epsilon\right] \...
2
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0answers
16 views
Convert a PAC-learning algorithm into another one which requires no knowledge of the parameter
This is part of the exercise 2.13 in the book Foundations of Machine Learning (page 28). You can refer to chapter 2 for the notations.
Consider a family of concept classes $\left\{\mathcal{C}_{s}\...
0
votes
1answer
26 views
Normalizing flow probabilities
I am using a normalizing flow (Neural Spline Flows) to approximate a probability and after some training, the average loss is around 0.5 (so logprob = -0.5). However, when I am trying it on some new ...
2
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0answers
21 views
What are some approaches to estimate the transition and observation probabilities in POMDP?
What are some common approaches to estimate the transition or observation probabilities, when the probabilities are not exactly known?
When realizing a POMDP model, the state model needs additional ...
1
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0answers
24 views
Can anyone explain the pixelwise accuracy metric used in this paper? Also a question to the KL Divergence Loss
So I am making a project based on this paper:
https://arxiv.org/ftp/arxiv/papers/1901/1901.07761.pdf
In this paper, a U-Net is used to generate optimized mechanical structures. I am trying to ...
0
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2answers
45 views
How can I convert the probability score between 0 to 1 to another format?
I have trained a multi-class CNN model using fastai. The model splits out probabilites for each of the three classes, which, of course, sum up to 1. The class with highest probability becomes the ...
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0answers
12 views
Is there an algorithm for “contextual recognition” with probabilities?
Example 1
An object is composed of 3 sub-objects.
Object 1: 90% looks like an eye 10% looks like a wheel
Object 2: 50% looks like an eye 50% looks like a wheel
Object 3: 90% looks like a mouth 10% ...
2
votes
2answers
144 views
What is probability distribution in machine learning?
If we were learning or working in machine learning field then we frequently come across this term probability distribution. I know what probability, conditional probability and probability ...
0
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0answers
15 views
Probabilistic classification - normalize results
I have a probabilistic classifier that produces a distribution over my 3 classes - C1, C2, C3.
I want to compare some new points I'm classifying to each other, to see which one is the best fit for a ...
2
votes
1answer
59 views
Why is the entire area of a join probability distribution considered when it comes to calculating misclassification?
In the image given below, I do not understand a few things
1) Why is an entire area colored to signify misclassification? For the given decision boundary, only the points between $x_0$ and the ...
1
vote
2answers
99 views
What to do when PDFs are not Gaussian/Normal in Naive Bayes Classifier
While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, ...
1
vote
0answers
39 views
Unique game problem (ML, DP, PP etc)
Looking for a solution to my below game problem. I believe it to require some sort of reinforcement learning, dynamic programming, or probabilistic programming solution, but am unsure... This is my ...
1
vote
1answer
64 views
Is there an AI model with “certainty” built in?
If I see a hundred elephants and fifty of them are grey I'd say the probability of an elephant being grey is 50%. And my certainty of that probability is high.
However, if I see two elephants and one ...
3
votes
1answer
54 views
Viterbi versus filtering
In Chapter 15 of Russel and Norvig's Artificial Intelligence -- A Modern Approach (Third Edition), they describe three basic tasks in temporal inference:
Filtering,
Likelihood, and
Finding the ...
1
vote
2answers
166 views
Why are VAE's useful?
I am not sure I understand what is the advantage of using a VAE's over a deterministic Auto Encoder? For example, assuming we have just 2 labels, a deterministic Auto Encoder will always map a given ...
1
vote
1answer
85 views
The problem with the Gambler's Problem in RL
Recently I simulated the Gambler's Problem in RL:
Now, the problem is, the curve does not at all appear the way as given in the book. The "best policy" curve appears a lot more undulating than it is ...
1
vote
3answers
197 views
How can I develop a prediction algorithm for a game of chance?
How can I develop a prediction algorithm in the case of games of chance? Suppose there is a 50:50 chance of winning. Is there way of creating a prediction algorithm?
1
vote
2answers
37 views
Was Nils Nilsson wrong with probabilistic knowledge representation?
He starts his paper from 1987 [1] with a reference to the PROSPECTOR expert system which was using Bayes rules to handle uncertain knowledge. Then he explains the idea behind probabilistic logic which ...
0
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2answers
64 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:
...
1
vote
0answers
13 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: <...
2
votes
2answers
56 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 ...
1
vote
2answers
208 views
Reinforcement Learning (RL) how to obtain $p(s',r|s,a)$
I am trying to study the book Reinforcement Learning: An Introduction (Sutton & Barto, 2018). In chapter 3.1 the authors state the following exercise
Exercise 3.5 Give a table analogous to that ...
1
vote
1answer
99 views
What are the meanings of these (P(x;y), P(x;y,z),P(x,y;z))?
I was reading a machine learning book that uses probabilities like these:
$P(x;y), P(x;y,z), P(x,y;z)$
I couldn't find what they are and how can I read and understand them?
Apart from the context, ...
3
votes
1answer
41 views
SEIF motion update algorithm doubt
I want to implement Sparse Extended information slam. There is four step to implement it. The algorithm is available in Probabilistic Robotics Book at page 310, Table 12.3.
In this algorithm line no:...
5
votes
1answer
526 views
How does the Dempster-Shafer theory of evidence differ from the Bayesian reasoning under uncertainty?
Dempster–Shafer theory (wiki)
Bayesian probability (wiki)
How do these two methods handle uncertainty in regard to information fusion?
8
votes
1answer
1k views
Is Nassim Taleb right about AI not being able to accurately predict certain types of distributions?
So Taleb has two heuristics to generally describe data distributions. One is Mediocristan, which basically means things that are on a Gaussian distribution such as height and/or weight of people.
The ...
5
votes
3answers
624 views
Is the singularity concept mathematically flawed? [closed]
In Comes IQ
When the concept of Intelligence Quotient arose it was based on this approximation.
Each human being has a number that quantifies their intelligence relative to a fixed norm, and, ...
-1
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1answer
51 views
How important will statistical learning be to a conscious AI?
Deep learning is based on getting a large number of samples and essentially making statistical deductions and outputting probabilities.
On the other hand we have formal programming languages like ...
2
votes
1answer
217 views
Reinforcement Learning over an MDP that is actually a POMDP
Look at Breakout:
We know that the underlying world behaves like an MDP, because for the evolution of the system it just need to know which is the current state, i.e. position, speed and speed ...
3
votes
2answers
146 views
What does the argmax of the expectation of the log likelihood mean?
What does the following equation mean? What does each part of the formula represent or mean?
$$\theta^* = \underset {\theta}{\arg \max} \Bbb E_{x \sim p_{data}} \log {p_{model}(x|\theta) }$$
2
votes
1answer
187 views
How can I improve this word-prediction AI?
I'm relatively new to AI, and I've tried to create one that "speaks". Here's how it works:
1. Get training data e.g 'Jim ran to the shop to buy candy'
2. The data gets split into overlapping 'chains' ...
7
votes
2answers
271 views
Are probabilistic models dead ends in AI?
I am a strong believer of Marvin Minsky's idea about Artificial General Intelligence (AGI) and one of his thoughts was that probabilistic models are dead ends in the field of AGI.
I would really ...
7
votes
2answers
2k views
What is a Markov chain and how can it be used in creating artificial intelligence?
I believe a Markov chain is a sequence of events where each subsequent event depends probabilistically on the current event. What are examples of the application of a Markov chain and can it be used ...
