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Anna University Regulation 2013 Information Technology (IT) MA6453 PQT Notes for all 5 units are provided below. Download link for IT 4thSEM MA6453 Probability & Queueing Theory Lecture Notes are listed down for students to make perfect utilization and score maximum marks with our study materials.
MA6453 PROBABILITY AND QUEUEING THEORY L T P C 3 1 0 4
OBJECTIVES:
To provide the required mathematical support in real life problems and develop probabilistic models which can be used in several areas of science and engineering.
UNIT I RANDOM VARIABLES 9+3
Discrete and continuous random variables – Moments – Moment generating functions – Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal distributions.
UNIT II TWO – DIMENSIONAL RANDOM VARIABLES 9+3
Joint distributions – Marginal and conditional distributions – Covariance – Correlation and Linear regression – Transformation of random variables.
UNIT III RANDOM PROCESSES 9+3
Classification – Stationary process – Markov process – Poisson process – Discrete parameter Markov chain – Chapman Kolmogorov equations – Limiting distributions.
UNIT IV QUEUEING MODELS 9+3
Markovian queues – Birth and Death processes – Single and multiple server queueing models – Little‟s formula – Queues with finite waiting rooms – Queues with impatient customers: Balking and reneging.
UNIT V ADVANCED QUEUEING MODELS 9+3
Finite source models – M/G/1 queue – Pollaczek Khinchin formula – M/D/1 and M/EK/1 as special cases – Series queues – Open Jackson networks.
TOTAL (L:45+T:15): 60 PERIODS
OUTCOMES:
The students will have a fundamental knowledge of the probability concepts.
Acquire skills in analyzing queueing models.
It also helps to understand and characterize phenomenon which evolve with respect to time in a probabilistic manner.
TEXT BOOKS:
1. Ibe. O.C., “Fundamentals of Applied Probability and Random Processes”, Elsevier, 1st Indian Reprint, 2007.
2. Gross. D. and Harris. C.M., “Fundamentals of Queueing Theory”, Wiley Student edition, 2004.
REFERENCES:
1. Robertazzi, “Computer Networks and Systems: Queueing Theory and Performance Evaluation”, , 3rd Edition, Springer, 2006.
2. Taha. H.A., “Operations Research”, 8th Edition, Pearson Education, Asia, 2007.
3. Trivedi.K.S., “Probability and Statistics with Reliability, Queueing and Computer Science Applications”, 2nd Edition, John Wiley and Sons, 2002.
4. Hwei Hsu, “Schaum‟s Outline of Theory and Problems of Probability, Random Variables and Random Processes”, Tata McGraw Hill Edition, New Delhi, 2004.
5. Yates. R.D. and Goodman. D. J., “Probability and Stochastic Processes”, 2nd Edition, Wiley India Pvt. Ltd., Bangalore, 2012.
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Anna University Regulation 2013 Computer Science Engineering (CSE) MA6453 PQT 2marks & 16marks for all 5 units are provided below. Download link for CSE 4th SEM MA6453 PROBABILITY AND QUEUEING THEORY Short answers, Question Bank are listed down for students to make perfect utilization and score maximum marks with our study materials.
MA6453 PROBABILITY AND QUEUEING THEORY QUESTION BANK
UNIT-III 2-marks
RANDOM PROCESSES
1. Define random process and its classification
Random process is a function of time and the outcomes of a random experiment. There are four types of random process
1. Discrete Random sequence
2. Continuous Random sequence
3. Discrete random process
4. Continuous random process.
2. Define (i) Continuous time random process (2) Discrete random process.
In Continuous random process, X and Time set T are continuous. Ex. The maximum temperature of a place at (0,t). In discrete random process, X is
In discrete random process, X is discrete and the time set is continuous. Ex. Number of telephone calls in (0,t).
3. Define stationary process
A random process is called stationary if all its statistical properties do not change with time.
5. Define wide sense stationary process.
A random process X(t) is called WSS if E[X(t)]=constant and Rxx;t,t+τͿ is a fuŶctioŶ of τ.
6. Define Markov process
A random process in which the future value depends only on the present value but not on the past value is called Markov process.
7. Define Transition probability matrix.
The TPM of a markov chain is P=(Pij) where Pij≥0 aŶd ¦Pij 1. Here Pij denote the one step transition probability.
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