# FME in Probability - Conditionals in Natural Language - Comments

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• haruspex
In summary, the PF Insights post discusses common errors made in understanding probability, specifically in regards to conditional statements in natural language. The author also shares an interesting perspective on how the amount of information gained from a probability statement can vary depending on the specific details given.
haruspex
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haruspex submitted a new PF Insights post

Frequently Made Errors in Probability - Conditionals in Natural Language

Continue reading the Original PF Insights Post.

Greg Bernhardt and mfb
Extremely unique topic!

Good read. Probability can be so counter-intuitive. One thing that's helped me a lot is seeing the log reciprocal relation between probability of an event, and the information in it. If I tell you that of Susan's two children, at least one is a girl, then I have given you log2(4/3) bits of information.(there are 3 of 4 possibilities where at least one is a girl, take reciprocal and log) If on the other hand you see the older one, and see she is a girl, you have gained log2(4/2) bits of information. (50/50 chance the other one is) Where does the extra information come from in the latter case? It comes from you having the which-child information you don't have in the first one: You know the older child is the one that's a girl. It gets a little weird though when you see a child of Susan who is a girl, but don't know which it is in terms of the predefined younger/older variable. You're back to the log(4/3) bits of information. Its a weird way to look at the world.

## 1. What is FME in probability?

FME stands for "Fuzzy Measure of Evidence" and is a mathematical concept used in probability theory to measure the degree of uncertainty or evidence for a given event.

## 2. How are conditionals used in natural language?

Conditionals in natural language refer to statements or propositions that express a relationship between two events or conditions, often using words like "if," "when," or "unless." These statements are used to describe the likelihood or consequences of certain events happening based on the fulfillment of certain conditions.

## 3. What is the significance of conditionals in probability?

Conditionals are important in probability because they allow us to make predictions or inferences about the likelihood of events occurring based on certain conditions being met. They also help us understand the relationship between different events and their probabilities.

## 4. How does FME relate to conditionals in natural language?

FME is a mathematical tool used to represent and measure the evidence or uncertainty associated with conditionals in natural language. It allows us to quantify the degree of support or evidence for a conditional statement and make more accurate probabilistic predictions.

## 5. What is the role of comments in FME and conditionals?

Comments in FME and conditionals help to clarify and explain the reasoning behind certain decisions or assumptions made in a probabilistic model. They provide context and rationale for the use of certain conditionals and FME values, making the model more transparent and interpretable.

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