Probability Theory and Stochastic Processes Books and Syllabus PDF

Checkout the Probability and Stochastic Processes Books for Reference purpose. In this article, we are providing the PTSP Textbooks, Books, Syllabus, and Reference books for Free Download. Probability Theory and Stochastic Processes is one of the important subjects for Engineering Students. Because of the importance of this subject, many Universities added this syllabus in their Academics. Probability and Stochastic Processes is also useful to most of the students, who are preparing for Competitive Exams.

Probability Theory and Stochastic Processes Books List

In this section, we are providing the important Probability Theory and Stochastic Processes Books for Free Download as a reference purpose in pdf format. The author’s clearly explained Probability and Stochastic Processes subject by using the simple language.

Probability Theory and Stochastic Process Textbook by oliver knill.

Probability, Random Variables and Stochastic Processes by Athanasios Papoulis and S. Unnikrishna Pillai.

Theory of Probability and Stochastic Processes by Pradip Kumar Ghosh.

Probability and Statistics for Engineers and Scientists by Ronald E. Walpole.

Probability Methods of Signal and System Analysis Textbook by George R.Cooper, Clave D.MC Gillem.

Probability and Random Processes Book by Scott Miller, Donald Childers.

Probability and Statistics by  Morris H. DeGroot, Mark J. Schervish.

Recommended Books

Probability Theory and Stochastic Processes Syllabus.


Probability and Random Variable

Probability: Probability introduced through Sets and Relative Frequency, Experiments and Sample Spaces, Discrete and Continuous Sample Spaces, Events, Probability Definitions and Axioms, Mathematical Model of Experiments, Probability as a Relative Frequency, Joint Probability, Conditional Probability, Total Probability, Bayes’ Theorem, Independent Events.

Random Variable: Definition of a Random Variable, Conditions for a Function to be a Random Variable, Discrete, Continuous and Mixed Random Variables


Distribution & Density Functions and Operation on One Random Variable – Expectations

Distribution & Density Functions: Distribution and Density functions and their Properties – Binomial, Poisson, Uniform, Gaussian, Exponential, Rayleigh and Conditional Distribution, Methods of defining Conditional Event, Conditional Density, Properties.

Operation on One Random Variable – Expectations: Introduction, Expected Value of a Random Variable, Function of a Random Variable, Moments about the Origin, Central Moments, Variance and Skew, Chebychev’s Inequality, Characteristic Function, Moment Generating Function, Transformations of a Random Variable: Monotonic Transformations for a Continuous Random Variable, Non-monotonic Transformations of Continuous Random Variable, Transformation of a Discrete Random Variable.


Multiple Random Variables and Operations

Multiple Random Variables: Vector Random Variables, Joint Distribution Function, Properties of Joint Distribution, Marginal Distribution Functions, Conditional Distribution and Density – Point Conditioning, Conditional Distribution and Density – Interval conditioning, Statistical Independence, Sum of Two Random Variables, Sum of Several Random Variables, Central Limit Theorem (Proof not expected), Unequal Distribution, Equal Distributions.

Operations on Multiple Random Variables: Expected Value of a Function of Random Variables: Joint Moments about the Origin, Joint Central Moments, Joint Characteristic Functions, Jointly Gaussian Random Variables: Two Random Variables case, N Random Variable case, Properties, Transformations of Multiple Random Variables, Linear Transformations of Gaussian Random Variables.


Stochastic Processes – Temporal Characteristics: 

The Stochastic Process Concept, Classification of Processes, Deterministic and Nondeterministic Processes, Distribution and Density Functions, Concept of Stationarity and Statistical Independence, First-Order Stationary Processes, Second-Order and Wide-Sense Stationarity, Nth Order and Strict-Sense Stationarity, Time Averages and Ergodicity, Mean-Ergodic Processes, Correlation-Ergodic Processes, Autocorrelation Function and its Properties, Cross-Correlation Function and its Properties, Covariance and its Properties, Linear System Response of Mean and Mean-squared Value, Autocorrelation Function, Cross-Correlation Functions, Gaussian Random Processes, Poisson Random Process.


Stochastic Processes – Spectral Characteristics:

Power Spectrum: Properties, Relationship between Power Spectrum and Autocorrelation Function, Cross-Power Density Spectrum, Properties, Relationship between Cross-Power Spectrum and Cross-Correlation Function, Spectral Characteristics of System Response: Power Density Spectrum of Response, Cross-Power Spectral Density of Input and Output of a Linear System.

These are the Important Probability Theory and Stochastic Processes Books. If you have any doubts about this, please let us know.

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