Modules / Lectures


Sl.No Chapter Name MP4 Download
1Lecture-01-Basic principles of countingDownload
2Lecture-02-Sample space , events, axioms of probabilityDownload
3Lecture-03-Conditional probability, Independence of events. Download
4Lecture-04-Random variables, cumulative density function, expected valueDownload
5Lecture-05-Discrete random variables and their distributionsDownload
6Lecture-06-Discrete random variables and their distributionsDownload
7Lecture-07-Discrete random variables and their distributionsDownload
8Lecture-08-Continuous random variables and their distributions.Download
9Lecture-09-Continuous random variables and their distributions.Download
10Lecture-10-Continuous random variables and their distributions.Download
11Lecture-11-Function of random variables, Momement generating functionDownload
12Lecture-12-Jointly distributed random variables, Independent r. v. and their sumsDownload
13Lecture-13-Independent r. v. and their sums.Download
14Lecture-14-Chi – square r. v., sums of independent normal r. v., Conditional distr.Download
15Lecture-15 Conditional disti, Joint distr. of functions of r. v., Order statisticsDownload
16Lecture-16-Order statistics, Covariance and correlation. Download
17Lecture-17-Covariance, Correlation, Cauchy- Schwarz inequalities, Conditional expectation.Download
18Lecture-18-Conditional expectation, Best linear predictorDownload
19Lecture-19-Inequalities and bounds.Download
20Lecture-20-Convergence and limit theoremsDownload
21Lecture-21-Central limit theoremDownload
22Lecture-22-Applications of central limit theoremDownload
23Lecture-23-Strong law of large numbers, Joint mgf.Download
24Lecture-24-ConvolutionsDownload
25Lecture-25-Stochastic processes: Markov process. Download
26Lecture-26-Transition and state probabilities. Download
27Lecture-27-State prob., First passage and First return probDownload
28Lecture-28-First passage and First return prob. Classification of states.Download
29Lecture-29-Random walk, periodic and null states.Download
30Lecture-30-Reducible Markov chainsDownload
31Lecture-31-Time reversible Markov chainsDownload
32Lecture-32-Poisson ProcessesDownload
33Lecture-33-Inter-arrival times, Properties of Poisson processesDownload
34Lecture-34-Queuing Models: M/M/I, Birth and death process, Little’s formulaeDownload
35Lecture-35-Analysis of L, Lq ,W and Wq , M/M/S modelDownload
36Lecture-36-M/M/S , M/M/I/K modelsDownload
37Lecture-37-M/M/I/K and M/M/S/K modelsDownload
38Lecture-38-Application to reliability theory failure lawDownload
39Lecture-39-Exponential failure law, Weibull lawDownload
40Lecture-40-Reliability of systemsDownload

Sl.No Chapter Name English
1Lecture-01-Basic principles of countingPDF unavailable
2Lecture-02-Sample space , events, axioms of probabilityPDF unavailable
3Lecture-03-Conditional probability, Independence of events. PDF unavailable
4Lecture-04-Random variables, cumulative density function, expected valuePDF unavailable
5Lecture-05-Discrete random variables and their distributionsPDF unavailable
6Lecture-06-Discrete random variables and their distributionsPDF unavailable
7Lecture-07-Discrete random variables and their distributionsPDF unavailable
8Lecture-08-Continuous random variables and their distributions.PDF unavailable
9Lecture-09-Continuous random variables and their distributions.PDF unavailable
10Lecture-10-Continuous random variables and their distributions.PDF unavailable
11Lecture-11-Function of random variables, Momement generating functionPDF unavailable
12Lecture-12-Jointly distributed random variables, Independent r. v. and their sumsPDF unavailable
13Lecture-13-Independent r. v. and their sums.PDF unavailable
14Lecture-14-Chi – square r. v., sums of independent normal r. v., Conditional distr.PDF unavailable
15Lecture-15 Conditional disti, Joint distr. of functions of r. v., Order statisticsPDF unavailable
16Lecture-16-Order statistics, Covariance and correlation. PDF unavailable
17Lecture-17-Covariance, Correlation, Cauchy- Schwarz inequalities, Conditional expectation.PDF unavailable
18Lecture-18-Conditional expectation, Best linear predictorPDF unavailable
19Lecture-19-Inequalities and bounds.PDF unavailable
20Lecture-20-Convergence and limit theoremsPDF unavailable
21Lecture-21-Central limit theoremPDF unavailable
22Lecture-22-Applications of central limit theoremPDF unavailable
23Lecture-23-Strong law of large numbers, Joint mgf.PDF unavailable
24Lecture-24-ConvolutionsPDF unavailable
25Lecture-25-Stochastic processes: Markov process. PDF unavailable
26Lecture-26-Transition and state probabilities. PDF unavailable
27Lecture-27-State prob., First passage and First return probPDF unavailable
28Lecture-28-First passage and First return prob. Classification of states.PDF unavailable
29Lecture-29-Random walk, periodic and null states.PDF unavailable
30Lecture-30-Reducible Markov chainsPDF unavailable
31Lecture-31-Time reversible Markov chainsPDF unavailable
32Lecture-32-Poisson ProcessesPDF unavailable
33Lecture-33-Inter-arrival times, Properties of Poisson processesPDF unavailable
34Lecture-34-Queuing Models: M/M/I, Birth and death process, Little’s formulaePDF unavailable
35Lecture-35-Analysis of L, Lq ,W and Wq , M/M/S modelPDF unavailable
36Lecture-36-M/M/S , M/M/I/K modelsPDF unavailable
37Lecture-37-M/M/I/K and M/M/S/K modelsPDF unavailable
38Lecture-38-Application to reliability theory failure lawPDF unavailable
39Lecture-39-Exponential failure law, Weibull lawPDF unavailable
40Lecture-40-Reliability of systemsPDF unavailable


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7MarathiNot Available
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