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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Probability Distributions01:32

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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Updated: Sep 9, 2025

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Optimización robusta generalizada utilizando la noción de probabilidad de valor fijo

Davide La Torre1, Franklin Mendivil2, Matteo Rocca3

  • 1SKEMA Business School, Université Côte d'Azur Sophia Antipolis Campus, Sophia Antipolis, France.

Journal of optimization theory and applications
|September 2, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un marco sólido que utiliza probabilidades de valor fijo para estimar probabilidades inciertas. Ofrece una mejor toma de decisiones y resiliencia en el modelado financiero y la gestión de riesgos.

Palabras clave:
Optimización de la carteraMedida de riesgoLa robustezMedida de probabilidad de valor fijo

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Área de la Ciencia:

  • Estadísticas matemáticas
  • Matemáticas financieras
  • Teoría de la decisión

Sus antecedentes:

  • La estimación estadística de las probabilidades es desafiada por la incertidumbre y los valores desconocidos.
  • Los métodos existentes pueden carecer de robustez cuando se trata de información probabilística imprecisa.

Objetivo del estudio:

  • Proponer un nuevo concepto de robustez basado en probabilidades de valor fijo.
  • Proporcionar un marco unificado y versátil para la estimación estadística bajo incertidumbre.
  • Para obtener condiciones óptimas de convexidad y estabilidad para una mayor robustez.

Principales métodos:

  • Utilizando el marco de probabilidades de valor fijo.
  • Empleando técnicas de escalarización para probabilidades de valores fijos.
  • Derivar las condiciones óptimas y establecer las propiedades generalizadas de convexidad y estabilidad.

Principales resultados:

  • Un nuevo concepto unificado de robustez para la estimación probabilística.
  • Optimalidad, convexidad generalizada y condiciones de estabilidad derivadas de la escalarización.
  • Aplicabilidad demostrada en la gestión de carteras financieras y en la teoría de la medida del riesgo.

Conclusiones:

  • El marco de probabilidad de valores fijos propuesto ofrece un enfoque sólido para la estimación estadística.
  • Las condiciones derivadas mejoran la confiabilidad de los modelos probabilísticos en entornos inciertos.
  • Este marco proporciona potentes herramientas para optimizar las decisiones y garantizar la resiliencia en las finanzas y la gestión de riesgos.