• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
Binomial Random Variable
Binomial Random Variable

5.1
5.1

... We’ll label the students in the AP Statistics class from 01 to 28, and the remaining students from 29 to 95. (Numbers from 96 to 00 will be skipped.) Starting at the randomly selected row 139 and moving left to right across the row, we’ll look at pairs of digits until we come across two different va ...
CSC384: Intro to Artificial Intelligence Reasoning under Uncertainty
CSC384: Intro to Artificial Intelligence Reasoning under Uncertainty

... this tell us anything more about how likely it is that the element also has property B? ■ If B is independent of A then we have learned nothing new about the likelihood of the element being a member of B. ...
Lecture13
Lecture13

Mathematics for Business Decisions, Part II
Mathematics for Business Decisions, Part II

... $18,000. (i) Use NORMDIST and Graphing.xlsm to plot the p.d.f., f X (x) . (ii) Use NORMDIST and Graphing.xlsm to plot the c.d.f., FX (x) . Solution. 11.. Let X be an exponential random variable with parameter   5 . (i) Use Graphing.xlsm to plot the p.d.f., f X , over the interval [0, 40] . (ii) Us ...
Compound Probability March 10, 2014
Compound Probability March 10, 2014

... 2. Your drawer contains 6 red socks and 11 blue socks. It's too dark to see which are which, but you grab two anyway. What is the probability that both socks are red? ...
Probability of Simple Events
Probability of Simple Events

Inference V: MCMC Methods - CS
Inference V: MCMC Methods - CS

Fun Facts about discrete random variables and logs
Fun Facts about discrete random variables and logs

Bayesian Networks and Hidden Markov Models
Bayesian Networks and Hidden Markov Models

Lecture1
Lecture1

A Survey of Probability Concepts
A Survey of Probability Concepts

conditional entropy
conditional entropy

... A means of representing statistical phenomena inherent in a sample of data, namely p( y | x) A means of requiring that our model of the process exhibit these ...
p p - Columbia Statistics
p p - Columbia Statistics

Finding TFBS
Finding TFBS

Midterm 1 practice
Midterm 1 practice

Descriptive statistics
Descriptive statistics

ppt
ppt

19. P-values, Power, Sample Size
19. P-values, Power, Sample Size

Lecture 20 and 21 1 PCP theorems using Parallel Repetition 2 H
Lecture 20 and 21 1 PCP theorems using Parallel Repetition 2 H

... 1. Let φ be an instance of a 2CSP problem. It is said to have the projection property if for each constraint φr (y1 , y2 ) and each value of y1 there is an unique value of y2 such that φr (y1 , y2 ) = 1. In other words, knowing the value of y1 is enough to know the value of y2 such that φr (y1 , y2 ...
Markov and Chebyshev`s Inequalities
Markov and Chebyshev`s Inequalities

... Question: A biased coin is flipped 200 times consecutively, and comes up heads with probability 1/10 each time it is flipped. Give an upper bound the probability that it will come up heads at least 120 times. Solution: Let X be the r.v. that counts the number of heads. Recall: E(X ) = 200 ∗ (1/10) = ...
Math 511 Problem Set 7 Solutions
Math 511 Problem Set 7 Solutions

MSc. Econ: MATHEMATICAL STATISTICS, 1996 The Moment
MSc. Econ: MATHEMATICAL STATISTICS, 1996 The Moment

craps - probability.ca
craps - probability.ca

Bayes` Rule
Bayes` Rule

< 1 ... 44 45 46 47 48 49 50 51 52 ... 70 >

Probability box



A probability box (or p-box) is a characterization of an uncertain number consisting of both aleatoric and epistemic uncertainties that is often used in risk analysis or quantitative uncertainty modeling where numerical calculations must be performed. Probability bounds analysis is used to make arithmetic and logical calculations with p-boxes.An example p-box is shown in the figure at right for an uncertain number x consisting of a left (upper) bound and a right (lower) bound on the probability distribution for x. The bounds are coincident for values of x below 0 and above 24. The bounds may have almost any shapes, including step functions, so long as they are monotonically increasing and do not cross each other. A p-box is used to express simultaneously incertitude (epistemic uncertainty), which is represented by the breadth between the left and right edges of the p-box, and variability (aleatory uncertainty), which is represented by the overall slant of the p-box.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report