Knowledge Tree
probability distribution
Also known as: probability distribution, probability distributions
Facts (12)
Sources
Chapter 8 – Risk and Return – Fundamentals of Finance pressbooks.pub 3 facts
claimThe expected return of a stock can be calculated using a probability distribution of potential returns, such as a 30% probability of a 12% return, a 50% probability of a 7% return, and a 20% probability of a -5% return.
measurementA hypothetical stock with a 20% probability of a -10% return (recession), a 50% probability of a 5% return (stable growth), and a 30% probability of a 15% return (boom) serves as an example of a probability distribution for investment returns.
claimA probability distribution is a statistical function used in finance to describe all possible outcomes of an investment and the likelihood of each outcome occurring.
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 2 facts
claimDensity ratio estimation from finite samples is unstable when the probability distributions are distant from each other.
claimExisting methods for density ratio estimation based on incremental mixtures can be reinterpreted as iterating on a Riemannian manifold along a specific curve between two probability distributions.
Detecting hallucinations with LLM-as-a-judge: Prompt ... - Datadog datadoghq.com Aug 25, 2025 2 facts
claimDirectly manipulating the probability distribution of generated tokens in Large Language Models can negatively impact the model's performance and accuracy.
procedureLarge Language Models (LLMs) can be constrained to specific output formats by combining the Finite State Machine's (FSM) list of valid tokens with the model's probability distribution and setting the logprob or logit of invalid tokens to negative infinity.
Hallucination Causes: Why Language Models Fabricate Facts mbrenndoerfer.com Mar 15, 2026 2 facts
claimThe generation process in large language models introduces pressure to favor fluent hallucination over honest uncertainty because the process is a sequence of probability distributions where the model must select a token at each step, and the model lacks a mechanism to output 'I don't know'.
claimLarge language models generate the most statistically plausible answer to questions implying a factual answer exists, rather than expressing uncertainty, because their probability distribution over vocabulary always has a mode and lacks probability mass for an 'abstain' option.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 1 fact
claimIn neuro-symbolic AI systems with partially explicit intermediate representations, the intermediate representations typically consist of symbolic logic expressions, mathematical expressions, structured programs, logic circuits, probability distributions, virtual circuits, or virtual machine instructions.
Papers - Dr Vaishak Belle vaishakbelle.github.io 1 fact
referenceThe paper 'A Logic of Only-Believing over Arbitrary Probability Distributions' by Q. Feng, D. Liu, G. Lakemeyer, and V. Belle was published in the AAMAS proceedings in 2023.
Next Generation Investment Risk Management: Putting the 'Modern ... financialplanningassociation.org 1 fact
perspectiveInvestment portfolio models should allow users to select probability distributions other than the normal distribution to better accommodate asymmetry, skewness, and fat tails in asset returns.