Statistical Information Processing

GTU Study
KHUDKIBOOK
STATISTICAL INFORMATION PROCESSING 3710510
FREE PDF 3710510
Code: 3710510 Credits: 4 Semester 1 EACE

Statistical Information Processing

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Statistical Information Processing Syllabus — Unit-wise Content

The official GTU curriculum for Statistical Information Processing (subject code 3710510) as prescribed for EACE · Semester 1 divides the course into 4 units, covering 46 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (3710510)

Unit 1 Review of random variables

6 lecture hours

  • Probability Concepts
  • distribution and density functions
  • uncorrelated and orthogonal random variables
  • Vector-space representation of Random variables
  • Vector quantization
  • Tchebaychef inequality theorem
  • Central Limit theorem
  • Discrete &Continuous Random Variables

Unit 2 Random process

6 lecture hours

  • Discrete-Time Random Processes Stationary process
  • autocorrelation and auto covariance functions
  • Spectral representation of random signals
  • Properties of power spectral density
  • Gaussian Process and White noise process

Unit 3 Statistical Decision Theory

9 lecture hours

  • Bayes’ Criterion
  • Binary Hypothesis Testing
  • M- ary Hypothesis Testing
  • Minimax Criterion
  • Neyman-Pearson Criterion
  • Composite Hypothesis Testing
  • Parameter Estimation Theory: Maximum Likelihood Estimation
  • Generalized Likelihood Ratio Test
  • Some Criteria for Good Estimators
  • Bayes’ Estimation Minimum Mean-Square Error Estimate
  • Mean Absolute Value of Error Estimate Maximum A Posteriori Estimate
  • Multiple Parameter Estimation Best Linear Unbiased Estimator
  • Least-Square Estimation Recursive Least-Square Estimator

Unit 4 Spectral analysis

6 lecture hours

  • Estimated autocorrelation function
  • Averaging the periodogram (Bartlett Method)
  • Welch modification
  • Parametric method
  • AR(p) spectral estimation and detection of Harmonic signals
  • References Books:  Papoulis and S.U
  • Random Variables and Stochastic Processes”
  • 4th Edition
  • Ingle and S.M
  • “Statistical and Adaptive Signal Processing”
  • 2000.  Mourad Barkat
  • “Signal Detection and Estimation”
  • Artech House
  • 2nd Edition
  • “Information theory and reliable communication”
  • 1st edition
  • MacWilliams and N
  • “The Theory of Error-Correcting Codes”
  • New York
  • “Elementary Number Theory”

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