Let’s break down how subjective randomness allows us to cut out all other influences. Remember that our definition of subjective randomness had 3 parts: 1) sensitivity to conditions (or influences); 2) non-repetition of conditions; and 3) a lack of knowledge of the conditions. In this post, we’ll look at just part 1 of this definition. …
Randomness is pretty important. Science would fall apart without it. Not just because randomised control trials wouldn’t be randomised, but because almost any experiment would produce junk. Say Galileo dropped his spheres of different weights, but their falls were affected by other (external) things non-randomly. The results would be biased or inconsistent, and we might …
In the last post, we saw that the choices we make aren’t free. That is, our choices (like how to get to work) are influenced by other things. Sometimes these things are obvious, like the weather or how quickly we need to get somewhere. Sometimes they are more subtle, like whether we had a good …
You move to a new city to start an exciting new job — Congratulations! — and you find a place to stay that the map suggests is about a 20 minute walk to work. Now that you’re in your new place, you check if there’s a quicker way to get in, and find a bus …
It can sometimes be hard to understand and remember the terms that statisticians use. This is understandable: statistics works with a lot of abstract concepts. But it helps when the terms are descriptive. For example, we have random variable: a value that varies depending on random events. We have estimator: a procedure or set of …
You take a peek out the window. It looks pleasant and calm. Fluffy white clouds shade the sun, birds are chirping and the trees are gently swaying in the breeze. You decide to head out for a walk. After a pleasant half hour out in the mild weather, you notice the wind pick up and …
Here is a Big Red Button: Don’t press it, wait till the very end of this post. What do you think the red button does? You might not know what to expect. Still, you will have some ideas, and you will assign more weight to some than others. These are your prior beliefs. Essentially, the …
A Bayesian network is traditionally defined as a directed acyclic graph (DAG), made up of nodes and arrows, coupled with discrete conditional probability tables (CPTs). If an arrow starts at one node and points to another, the first node is called the parent, and the second the child — but only for that arrow. Each …
Continue reading ‘A Bite-Sized Introduction to Bayesian Networks’ »
A little bit of an unintentional pause in the release rhythm, but we’re hopefully back to an ordinary schedule now. This release has a few API updates, plus one addition to the GUI: the ability to highlight d-connected nodes. In case you’re not familiar with d-connectedness: Two nodes are d-connected (given existing evidence) if it …
The main additions in this release revolve around being able to change the type of a node (still in progress), and improvements to editing node definitions. There is also a tiny bit of (non-functioning) foreshadowing for some bigger changes to node definitions. Make-Believe R23 [or view latest] Changes: Add editing for function tables (deterministic/discrete and …