MA4151 APPLIED PROBABILITY AND STATISTICS FOR COMPUTER SCIENCE ENGINEERS


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     S.Santhosh (Admin) 
Important questions 
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Unit 1
1.Pseudo inverse least square approximations
2.Eigenvalues and Eigenvector**
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3.canonical form related problems**
UNIT-2
1.Exponential Distribution( mean, variance)**
2. Poisson distribution
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3. binomial ,geometric distribution
rare 
4. Bayes theorem

Unit 3 
1. Correlative and regression**
2. Covariance
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UNIT-4
1.Small and Large samples Tests based on Normal, t, Chi square and F distributions
2.diff b/w mean variance
UNIT-5
1.Random vectors and matrices Mean vectors and covariance matrices  
2.Population principal components

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**Very important questions are bolded and may be asked based on this topic
PART-C

1.Compulsory Questions {a case study where the student will have to read and analyse the subject }
mostly asked from unit 4,5(OR) a Problem given

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Contact uS *These questions are expected for the exams This may or may not be asked for exams
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SYllabuS

UNIT I LINEAR ALGEBRA

norms Inner Products

Vector spaces factorization Eigenvalues using QR transformations generalized eigenvectors Canonical forms and applications pseudo inverse least square approximations. singular value decomposition

UNIT II PROBABILITY AND RANDOM VARIABLES

Probability variables properties distributions Axioms of probability Conditional probability - Baye's theorem Random Probability function Moments Moment generating functions and their Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal Function of a random variable.

UNIT III TWO DIMENSIONAL RANDOM VARIABLES

Joint distributions random variables Marginal and conditional distributions Regression curve Correlation. Functions of two dimensional

UNIT IV TESTING OF HYPOTHESIS

Sampling distributions Type I and Type II errors

Small and Large samples Tests based on Normal, t, Chi square and F distributions for testing of mean, variance and proportions Tests for independence of attributes and goodness of fit.

UNIT V MULTIVARIATE ANALYSIS POUCH KNOWLEDGE

Random vectors and matrices Mean vectors and covariance matrices Multivariate normal density and its properties Principal components Principal components from standardized variables. Population principal components

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