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関連する概念動画

Probability in Statistics01:14

Probability in Statistics

14.7K
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.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
14.7K
Probability Distributions01:32

Probability Distributions

7.9K
 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.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.9K
Probability Laws01:49

Probability Laws

41.7K
Overview
41.7K
Random Variables01:09

Random Variables

13.4K
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.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.4K
Randomized Experiments01:13

Randomized Experiments

7.2K
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.
Simple randomization
Simple...
7.2K
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.4K

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Updated: Sep 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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セット値確率の概念を用いた一般化ロバスト最適化

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
まとめ
この要約は機械生成です。

この研究は,不確実な確率を推定するためにセット値の確率を使用する堅牢な枠組みを導入します. 金融モデリングとリスク管理において 意思決定と回復力を向上させます

キーワード:
ポートフォリオの最適化リスク対策頑丈さ設定値の確率測定法

さらに関連する動画

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

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関連する実験動画

Last Updated: Sep 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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科学分野:

  • 数学的な統計
  • 金融数学
  • 意思決定理論

背景:

  • 確率の統計的見積もりは 不確実性と未知値によって困難です
  • 不正確な確率情報に対処する際には,既存の方法には強度が欠けることがあります.

研究 の 目的:

  • 定数値の確率に基づいた 堅実性の新しい概念を提案する.
  • 不確実な状況下での統計的見積もりのための統一され,多面的な枠組みを提供すること.
  • 最適性,凸性,安定性の条件を導き出します. 強化された頑丈性のために.

主な方法:

  • 設定値の確率のフレームワークを使用します.
  • 設定値の確率のスケラライゼーション技術を使用する.
  • 最適性条件を導き出し,一般的な凸性と安定性特性を確立する.

主要な成果:

  • 概率的な見積もりのための新しい,統一された概念です.
  • スキャラライゼーションから派生した最適性,一般的凸性,および安定性条件.
  • 金融ポートフォリオ管理とリスク測定理論の適用が実証されています.

結論:

  • 提案されたセット価値の確率の枠組みは,統計的推定に堅固なアプローチを提供します.
  • 導き出された条件は不確実な環境における確率モデルの信頼性を高めます.
  • この枠組みは意思決定の最適化と金融とリスク管理の回復力を確保するための強力なツールを提供します.