# Questions tagged [algorithms]

An unambiguous list of computational steps involved in finding a solution to a class of problems.

898 questions
Filter by
Sorted by
Tagged with
6 views

### Why a fully factorised approximation in message passing algorithms?

I would like to understand how to do Bayesian inference in a Bayesian network that contains a mixture of discrete and continuous random variables. There are two algorithms that seem like they do ...
1 vote
10 views

### Formal approach to message passing algorithms

I'm trying to understand message passing algorithms, especially for the specific application of performing conditioning in a Bayesian network. My question is wether there is a mathematically precise ...
21 views

### How to fix the tree structure for a tree-based algorithm?

Background Some of our BI analysts and most of our managers are interested in making explainable predictions. One of our colleagues proposed an approach based on individual tree leaves from a tree-...
63 views

### Combinations from different sets with weightings

Imagine the following scenario: I want to create 1000 unique combinations of clothing. The combinations would include the following categories: hats, shirts, shorts, socks and shoes. Each combination ...
44 views

### Robust distance weighted mean

Given a data sample $\{x_i\}_1^n$, instead of hard omitting outliers by e.g. trimming, one can form a weighted average where we soft penalize observations out in the tails. \begin{align} \mu = \frac{...
1 vote
25 views

### no free lunch theorem version 2

Let data-generating function $f$ be fixed, $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $A$ is set of all possible deterministic learning algorithms and $h:X\rightarrow Y$ is a classifier trained by the ...
1 vote
7 views

### how to implement a next position predictor machine learning model? [closed]

I am trying to implement next position predictor base on machine Learning algorithms.I want to know how can i start building my model ?
1 vote
175 views

### No free lunch theorem proof

Assume that learning algorithm $A$ is fixed. Let $D = \{(x_1,y_1),...,(x_N,y_N)\}$, $F$ is set of a data-generating functions(meaning $f \in F$ then $f(x_i) = y_i$ and that functions in $F$ are ...
9 views

### DoE for optimization / control approach?

I'm wondering whether a DoE approach could somehow be used as kind of an optimization algorithm? One of my current tasks is to find a set of five parameter which max a sixth one (see here for more: ...
23 views