'Discrete distribution' presentation slideshows

Lesson 4:

Lesson 4:. Discrete Probability Distributions. Outline. Random Variables and probability distribution. A random variable is a numerical value determined by the outcome of an experiment. A random variable is often denoted by a capital letter, e.g., X or Y.

1.28k views • 84 slides

Lesson 4:

Lesson 4:. Discrete Probability Distributions. Outline. Random Variables and probability distribution. A random variable is a numerical value determined by the outcome of an experiment. A random variable is often denoted by a capital letter, e.g., X or Y.

685 views • 62 slides

Operations Management Waiting-Line Models Module D

Operations Management Waiting-Line Models Module D. Outline. Characteristics of a Waiting-Line System. Arrival characteristics. Waiting-Line characteristics. Service facility characteristics. Waiting Line (Queuing) Models. M/M/1: One server. M/M/2: Two servers. M/M/S: S servers.

2.65k views • 57 slides

Operations Management Waiting-Line Models Module D

Operations Management Waiting-Line Models Module D. Outline. Characteristics of a Waiting-Line System. Arrival characteristics. Waiting-Line characteristics. Service facility characteristics. Waiting Line (Queuing) Models. M/M/1: One server. M/M/2: Two servers. M/M/S: S servers.

1.07k views • 57 slides

Operations Management Waiting-Line Models Module D

Operations Management Waiting-Line Models Module D. Outline. Characteristics of a Waiting-Line System. Arrival characteristics. Waiting-Line characteristics. Service facility characteristics. Waiting Line (Queuing) Models. M/M/1: One server. M/M/2: Two servers. M/M/S: S servers.

1.23k views • 57 slides

Random Variable and its Properties

Random Variable and its Properties. Prof. Dr. S. K. Bhattacharjee Department of Statistics University of Rajshahi. Random Variable and its Properties.

726 views • 54 slides

PROJECT RISK MANAGEMENT

PROJECT RISK MANAGEMENT. Ammar Bukhari. What is a Risk. RISK: Threat / Uncertainty of Outcome probability that an action or event, will adversely or beneficially affect an organization's ability to achieve its objectives. Risk vs Issue. Risk Future event May impact triple constraint

888 views • 52 slides

Advanced Artificial Intelligence

Advanced Artificial Intelligence. Lecture 6: Hidden Markov Models and Temporal Filtering. Class-On-A-Slide. X 1. X 2. X 3. X 4. X 5. E 1. E 2. E 3. E 4. E 5. Example: Minerva. Example: Robot Localization. Example: Groundhog. Example: Groundhog. Example: Groundhog. Overview.

809 views • 47 slides

Prepared by Lloyd R. Jaisingh

A PowerPoint Presentation Package to Accompany. Applied Statistics in Business & Economics, 4 th edition David P. Doane and Lori E. Seward. Prepared by Lloyd R. Jaisingh. Chapter Contents 6.1 Discrete Distributions 6.2 Uniform Distribution 6.3 Bernoulli Distribution

640 views • 46 slides

Time-dependent queue modelling

Time-dependent queue modelling. Dave Worthington Management Science Dept. Lancaster University d.worthington@lancaster.ac.uk. Time-dependent queue modelling -Outline. Motivation What is time-dependent behaviour ? Analytic methods Discrete time modelling (DTM) approach DTM-based task.

310 views • 30 slides

Pre-regression Basics

Pre-regression Basics. Random Vs. Non-random variables Stochastic Vs. Deterministic Relations Correlation Vs. Causation Regression Vs. Causation Types of Data Types of Variables The Scientific Method Necessary & Sufficient Conditions. Random Vs. Non-random Variables.

485 views • 29 slides

Pre-regression Basics

Pre-regression Basics. Random Vs. Non-random variables Stochastic Vs. Deterministic Relations Correlation Vs. Causation Regression Vs. Causation Types of Data Types of Variables The Scientific Method Necessary & Sufficient Conditions. Random Vs. Non-random Variables.

337 views • 29 slides

Continuous Probability Distributions

388 views • 25 slides

Lecture 25 Simulation and Modeling

Lecture 25 Simulation and Modeling. Md. Tanvir Al Amin, Lecturer, Dept. of CSE, BUET. CSE 411. Recap : Inverse Transform Method. Find cumulative distribution function Let it be F Find F -1 X = F -1 (R) is the desired variate where R ~ U(0,1) Other methods are Accept Reject technique

336 views • 24 slides

COIN-SMI 2006

COIN-SMI 2006. Alan King IBM TJ Watson Research Center. Optimization and Uncertainty. Many practical applications involve aspects of uncertainty and optimization. Examples just from EURO 2006: Hydropower Management (MA-43) Pension Fund Management (ME-43 and TB-43) Risk Functionals (TA-43)

337 views • 15 slides

Continuous Distributions

287 views • 14 slides

Lesson #12 Discrete Probability Distributions

Lesson #12 Discrete Probability Distributions. Y = # people in ER in an hour. Flip 2 coins, X = # heads. y P(y). x P(x). p 0 p 1 p 2 p 3 p 4 ?. 0 1 2 3 4 ?. 0 1 2. .25 .50 .25. x = 0, 1, 2. For discrete distributions, f(x) is called the probability function.

300 views • 14 slides

Rao-Blackwellized Particle Filtering Pieter Abbeel UC Berkeley EECS

Rao-Blackwellized Particle Filtering Pieter Abbeel UC Berkeley EECS Many slides adapted from Thrun , Burgard and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A. Particle Filters Recap.

285 views • 11 slides

The Normal Approximation

The Normal Approximation. To the Binomial Distribution. Recall: Binomial Distributions have: 1. Fixed number of trials 2. Independent Outcomes 3. Exactly two outcomes 4. Same probabilities for each trial. Binomial Distributions.

305 views • 10 slides

Compare The Discrete Probability Distribution Something Great /tutorialoutletdotcom

FOR MORE CLASSES VISIT www.tutorialoutlet.com Compare the discrete probability distribution to the continuous probability distribution, in terms of whether the probability of a specific value of X can be computed. A.The probability of a specific value of X occurring can be computed for a discrete distribution but it cannot be computed for a continuous distribution.

48 views • 3 slides