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.
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.
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.
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.
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.