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Probability & Statistics for Electrical Engineers Assignment Help,Probability & Statistics Homework Help


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Topics like Time Series & the assignment help on these topics is really helpful if you are struggling with the complex problems.

  • Probability & Statistics for Electrical Engineers
  • Anology to digital logic
  • basic probability and Venn diagrams
  • Conditional probability
  • Spy game, optional: detailed analysis of spy game
  • Bayes Rule
  • Monty Hall Matlab code
  • Random variables
  • Expectation
  • Variance
  • Binomial distribution
  • Normal (Gaussian) distribution
  • Some other distributions
  • Joint discrete random variables
  • Joint continuous random variables
  • Covariance of random variables
  • Linear combinations of random variables
  • Chebyshev's theorem
  • Introduction to statistics
  • Central limit theorem, sampling distributions
  • Examples using the different tables
  • Confidence intervals
  • Hypothesis testing
  • Hypothesis testing continued
  • Linear regression
  • Inference on linear regression parameters
  • Basic Concepts of Probability Theory
  • Specifying Random Experiments
  • The Axioms of Probability
  • Computing Probabilities Using Counting Methods
  • Conditional Probability
  • Independence of Events
  • Sequential Experiments
  • Synthesizing Randomness: Random Number Generators
  • Probabilities of Sequences of Events
  • Discrete Random Variables
  • The Notion of a Random Variable
  • Discrete Random Variables and Probability Mass Function
  • Expected Value and Moments of Discrete Random Variable
  • Conditional Probability Mass Function
  • Important Discrete Random Variables
  • Generation of Discrete Random Variables
  • One Random Variable
  • The Cumulative Distribution Function
  • The Probability Density Function
  • The Expected Value of X
  • Important Continuous Random Variables
  • Functions of a Random Variable
  • The Markov and Chebyshev Inequalities
  • Transform Methods
  • Basic Reliability Calculations
  • Computer Methods for Generating Random Variables
  • Entropy
  • Pairs of Random Variables
  • Two Random Variables
  • Pairs of Discrete Random Variables
  • The Joint cdf of X and Y
  • The Joint pdf of Two Continuous Random Variables
  • Independence of Two Random Variables
  • Joint Moments and Expected Values of a Function of Two Random
  • Variables
  • Conditional Probability and Conditional Expectation
  • Functions of Two Random Variables
  • Pairs of Jointly Gaussian Random Variables
  • Generating Independent Gaussian Random Variables
  • Vector Random Variables
  • Vector Random Variables
  • Functions of Several Random Variables
  • Expected Values of Vector Random Variables
  • Jointly Gaussian Random Vectors
  • Estimation of Random Variables
  • Generating Correlated Vector Random Variables
  • Sums of Random Variables and Long-Term Averages
  • Sums of Random Variables
  • The Sample Mean and the Laws of Large Numbers
  • Weak Law of Large Numbers
  • Strong Law of Large Numbers
  • The Central Limit Theorem
  • Central Limit Theorem
  • Convergence of Sequences of Random Variables
  • Long-Term Arrival Rates and Associated Averages
  • Calculating Distribution’s Using the Discrete Fourier
  • Transform
  • Statistics
  • Samples and Sampling Distributions
  • Parameter Estimation
  • Maximum Likelihood Estimation
  • Confidence Intervals
  • Hypothesis Testing
  • Bayesian Decision Methods
  • Testing the Fit of a Distribution to Data
  • Random Processes
  • Definition of a Random Process
  • Specifying a Random Process
  • Discrete-Time Processes: Sum Process, Binomial Counting Process,
  • and Random Walk
  • Poisson and Associated Random Processes
  • Gaussian Random Processes, Wiener Process
  • and Brownian Motion
  • Stationary Random Processes
  • Continuity, Derivatives, and Integrals of Random Processes
  • Time Averages of Random Processes and Ergodic Theorems
  • Fourier Series and Karhunen-Loeve Expansion
  • Generating Random Processes
  • Analysis and Processing of Random Signals
  • Power Spectral Density
  • Response of Linear Systems to Random Signals
  • Bandlimited Random Processes
  • Optimum Linear Systems
  • The Kalman Filter
  • Estimating the Power Spectral Density
  • Numerical Techniques for Processing Random Signals
  • Markov Chains
  • Markov Processes
  • Discrete-Time Markov Chains
  • Classes of States, Recurrence Properties, and Limiting
  • Probabilities 660
  • Continuous-Time Markov Chains
  • Time-Reversed Markov Chains
  • Numerical Techniques for Markov Chains
  • Queueing Theory
  • The Elements of a Queueing System
  • Little’s Formula
  • The M/M/1 Queue
  • Multi-Server Systems: M/M/c, M/M/c/c,
  • Finite-Source Queueing Systems
  • M/G/1 Queueing Systems
  • M/G/1 Analysis Using Embedded Markov Chains
  • Burke’s Theorem: Departures From M/M/c Systems
  • Networks of Queues: Jackson’s Theorem
  • Simulation and Data Analysis of Queueing Systems
  •  
 

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