Questions tagged [heuristic]

A heuristic is a general rule for making some sort of decision or judgement. Pioneering work in the study of heuristics was done by Amos Tversky and Daniel Kahneman.

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Do heuristic algorithms always get better solutions than greedy algorithms? [closed]

My question is as follows: I use the genetic algorithm (GA) and the greedy algorithm to solve the Travelling salesman problem (TSP problem), but I find that when the number of nodes is large, the ...
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A comprehension problem in Gambler's fallacy and calculating sample mean

The following problem and solution appears in Judgment under uncertainty: Heuristics and Biases; Page 24 The mean IQ of the population of eighth graders in a city is known to be 100. You have ...
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Silhouette score average : per cluster or not?

Wikipedia in English says that the average silhouette for k clusters is simply the average of the silhouette on all samples: $$ S(k) = {1\over{n}} \sum_{i=0}^{n-1} s(i) $$ where s(i) is the silhouette ...
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Applying Pearson's correlation, hypothesis tests etc. to non-iid data

(This is a very loose question. I'm aware that any theoretically sound answer to this will be specific to $(T,X)$ and involve sufficient assumptions on $X$ such as various ergodicity and mixing ...
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EM, MCMC and VB, What are their similarity, difference and use scenorio?

I am recently learning MCMC, and realized some connections between MCMC, EM and VB (variational Bayesian). However, I did not find any discussion that can clear my mind on their similarity, difference ...
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Which heuristic for the simulated portfolio weights?

I am trying to create a Simulated Portfolio Optimization based on Efficient Frontier on 50 stocks, which you can find the csv here. Yet it already takes me several minutes to get a suboptimal solution:...
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Hill Climbing with hysteresis

I'm trying to solve a specific problem related to my work in experimental physics. However, I'll try to keep my question as general as possible so that it is useful to a wider audience. If some ...
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43 views

What is a sensible way to truncate data to a region that fits a model?

I want to use an exponential decay model in python to relate the flow rate in a device to the mass left inside it, in particular $flow=a−b×e^{−c×mass}$ where a, b and c are the parameters of the model....
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Statistical validaty of heuristic labelling

I have an unlabelled dataset with over 10K observations. For label assignment, I used some heuristic that I devised from first principles. The heuristics where in the form of "If the attribute ...
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How to model a specific distribution using domain knowledge rules [closed]

Suppose I have a variable Y that I want to predict with a model using predictor variables X1, X2 and X3. I have a large set of Y-data and from this I know with some certainty and accuracy the ...
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Finding the maximum of a function $f(x)$ without analytically evaluating $f'(x)$

I'm an experimental physicist, trying to automate a relatively simple (but sensitive) optimization in my measurements that is currently done completely manually and takes up a lot of my time. I figure ...
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What Are Shrinking Heuristics

I have been working on a project with LibSVM and have noticed there is an option to train the SVM model with "shrinking heuristics" which are used to speed up the classifier training. After doing ...
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Under which condition does the heuristic find the true global minimum?

Consider the unconstrained programming problem with a high-dimensional, smooth function $f(x_1,x_2,\ldots x_N)$. Because $N$ is large, such kind of heuristics is sometimes used: -- Fix $x_2…x_N$ to ...
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What is the right name for “heuristics” with guaranteed improvement?

I am working on an algorithm which tries to improve existing predictive model. The predictive model is associated with several objectives (such as accuracy or model size) that can be optimized. Let'...
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How does Bayesian analysis make accurate predictions using subjectively chosen probabilities?

Since Kahneman and Tversky found that humans do not accurately assume probabilities, how can Bayes theorem use subjectively chosen probabilities to accurately predict things (like insurance policies), ...
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Is machine learning an heuristic method?

I'm asking this out of curiosity. In the past I have thought of an heuristic as a "quick and dirty" rule not based on data analysis, as opposed to a solution which uses machine learning or ...
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229 views

Maximizing Variance in Samples

I have a given set of numbers and I want to divide it into even n subsets, maximising the sum of each subset's variance. Can I do this better than creating each ...
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Name of a heuristic for rule learning

I have some rule learning code that evaluates a refinement to a rule with the following: Value = NL*TP-FP*PL. NL is the number of negatives in the data set. ...
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Why does the L2 norm heuristic work in measuring uniformity of probability distributions?

To start off, please go through this question regarding measuring non-uniformity in probability distributions. Among several good answers, user495285 has suggested a heuristic of simply taking the ...
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Optimizing while collecting data - optimization in a real world problem

I want to conduct a soil analysis using a different mix of let says Nutrition A, Nutrition B and Nutrition C. Since I can put for each nutrition multiple values, I cannot try out all the possible ...
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Using particle swarm optimization (PSO) h in neural network training?

Does using PSO have advantages/disadvantages over back-propagation when training neural networks? Please give your opinion if you have used PSO or other heuristic methods.
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What is a “high” adjusted Rand index score?

Does anyone know if there's a heuristic for a high or low adjusted Rand index? I understand this is rather subjective - and probably depends on the type of network data you're using. However, if ...
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Convergence diagnostic of Markov chain that converge to uniform

Let $\Omega$ be a finite state space, $(X_t)_{t\in\mathbb{N}}$ be a discrete-time Markov chain that converges to the uniform distribution, and $P$ be its transition matrix. I'm looking for different ...
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Do we have machine Learning models that benefit with more time?

Just a high level question, Given a human $H$ if you give them a question (and they have sufficient training), the more time you give them to work on the question (assuming all other needs are met ...
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Heuristics streaming data matching

I have an index composed by thousands of documents. Slightly modified copies of those documents are sent to my application in small chunks, and I need to check, from those chunks, which document has ...
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Developing a heuristic for maximizing the “covering” of a distribution

Context There's a board-game called Settlers of Catan in which players compete to be the first to gain 10 victory points by trading various resources in exchange for pieces (or cards) worth victory ...
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Is it better to select distributions based on theory, fit or something else?

This is bordering on a philosophical question, but I am interested in how others with more experience think about distribution selection. In some cases it seems clear that theory might work best (mice ...
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Should I use the Kernel Trick whenever possible for non-linear data?

I recently learned about the use of the Kernel trick, which maps data into higher dimensional spaces in an attempt to linearize the data in those dimensions. Are there any cases where I should avoid ...
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What is a good and efficient algorithm for a content based recommender?

I want to build a content based recommender in a restricted environment regarding cpu power and memory (to be specific: a mobile device, but it is not acceptable to build the recommender on a remote ...
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Conditions for Poisson approximation of the superposition of non-Poisson processes

It is well known that the superposition of $N$ Poisson processes is itself a Poisson process with an intensity given by $\sum_{n=1}^{N} \lambda _{n}$. Conversely a superposition including any non-...
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Why can't we trust our intuition with probability?

If ever there was a case where this become clear is with the Monty Hall problem. Even the great Paul Erdos got fooled by this problem. My question which may be difficult to answer is what is it ...