“Master Joint PDF in Minutes | Joint Density Function & Properties”
ECE by MVK
“Master Joint PDF in Minutes | Joint Density Function & Properties”
6:23
Joint Distribution Function for Exams | Simple Explanation + Properties
ECE by MVK
Joint Distribution Function for Exams | Simple Explanation + Properties
4:59
“Monotonic vs Non-Monotonic Transformation Explained”
ECE by MVK
“Monotonic vs Non-Monotonic Transformation Explained”
5:58
“Moment Generating Function (MGF) Explained | Definition| Probability”
ECE by MVK
“Moment Generating Function (MGF) Explained | Definition| Probability”
3:14
Characteristic Function of a Random Variable | Complete Guide
ECE by MVK
Characteristic Function of a Random Variable | Complete Guide
2:43
Properties of Expectation Explained | Linearity, Additivity & Key Rules | Probability Made Simple
ECE by MVK
Properties of Expectation Explained | Linearity, Additivity & Key Rules | Probability Made Simple
4:53
Rayleigh Random variable in terms of CDF and PDF
ECE by MVK
Rayleigh Random variable in terms of CDF and PDF
5:22
“Poisson Random Variable | Probability Mass Function (PMF)
ECE by MVK
“Poisson Random Variable | Probability Mass Function (PMF)
5:04
“Uniform Random Variable | Probability Density Function & CDF Explained with Example”
ECE by MVK
“Uniform Random Variable | Probability Density Function & CDF Explained with Example”
3:52
“Probability Density Function (PDF) & Cumulative Distribution Function (CDF) | Simple Explanation”
ECE by MVK
“Probability Density Function (PDF) & Cumulative Distribution Function (CDF) | Simple Explanation”
9:11
Random Variable in Probability | Discrete vs Continuous Explained with Examples
ECE by MVK
Random Variable in Probability | Discrete vs Continuous Explained with Examples
6:30
“Characteristic Function Explained | Probability & Random Variables Made Simple”
ECE by MVK
“Characteristic Function Explained | Probability & Random Variables Made Simple”
2:43
“Theorem of Total Probability | Definition, Formula, and Example”
ECE by MVK
“Theorem of Total Probability | Definition, Formula, and Example”
7:22
“Experiment and Its Types in Probability | Random & Deterministic Experiments Explained”
ECE by MVK
“Experiment and Its Types in Probability | Random & Deterministic Experiments Explained”
5:12
Equally Likely & Independent Events Explained | Probability Made Simple with Examples
ECE by MVK
Equally Likely & Independent Events Explained | Probability Made Simple with Examples
4:17
“Events in Probability | Mutually Exclusive & Exhaustive Events Explained with Examples”
ECE by MVK
“Events in Probability | Mutually Exclusive & Exhaustive Events Explained with Examples”
6:13
Sample Space Explained | Discrete & Continuous Sample Space with Examples | Probability Basics
ECE by MVK
Sample Space Explained | Discrete & Continuous Sample Space with Examples | Probability Basics
4:32
“Given Rₓₓ(τ)=(25τ²+36)/(625τ²+4) | Find Mean, Mean-Square & Variance | Complete Derivation”
ECE by MVK
“Given Rₓₓ(τ)=(25τ²+36)/(625τ²+4) | Find Mean, Mean-Square & Variance | Complete Derivation”
8:48
Given Rxx(τ)=625+16/(1+36τ²) | Find Mean and Variance | Step-by-Step Solution
ECE by MVK
Given Rxx(τ)=625+16/(1+36τ²) | Find Mean and Variance | Step-by-Step Solution
9:36
Is X(t) = kt a Wide Sense Stationary Process Random Process Example Explained Step by Step
ECE by MVK
Is X(t) = kt a Wide Sense Stationary Process Random Process Example Explained Step by Step
9:28
Wide Sense Stationary Random Process X(t) = Acosω₀t+Bsinω₀t Complete Derivation Explained
ECE by MVK
Wide Sense Stationary Random Process X(t) = Acosω₀t+Bsinω₀t Complete Derivation Explained
17:27
Cross Correlation Function in Random Process | Definition, Formula & Properties
ECE by MVK
Cross Correlation Function in Random Process | Definition, Formula & Properties
9:22
Autocorrelation Function – Definition, Formula & Properties | Exam & GATE Preparation
ECE by MVK
Autocorrelation Function – Definition, Formula & Properties | Exam & GATE Preparation
13:17
Is the random process X(t)=Acos(ωt+θ) wide-sense stationary or not?
ECE by MVK
Is the random process X(t)=Acos(ωt+θ) wide-sense stationary or not?
18:15
"Stationarity in Random Processes | First, Second & Wide Sense Stationary Explained with Examples"
ECE by MVK
"Stationarity in Random Processes | First, Second & Wide Sense Stationary Explained with Examples"
10:14
"Cumulative Distribution Function & Probability Density Function | Random Process Tutorial"
ECE by MVK
"Cumulative Distribution Function & Probability Density Function | Random Process Tutorial"
6:51
"🎯 Deterministic vs Non-Deterministic Processes | Probability & Random Processes Explained"
ECE by MVK
"🎯 Deterministic vs Non-Deterministic Processes | Probability & Random Processes Explained"
3:24
"📘 Classification of Random Processes | Probability & Random Processes Explained"
ECE by MVK
"📘 Classification of Random Processes | Probability & Random Processes Explained"
10:46
"📘 Cumulative Distribution Function (CDF) & Its Properties | Probability & Statistics Explained"
ECE by MVK
"📘 Cumulative Distribution Function (CDF) & Its Properties | Probability & Statistics Explained"
17:42
Bayes’ Theorem Explained with Solved Problem | Probability & Statistics Made Easy"-UNIT1
ECE by MVK
Bayes’ Theorem Explained with Solved Problem | Probability & Statistics Made Easy"-UNIT1
14:50
9. "📘 Marginal Distribution Functions from Joint PDF | Probability & Statistics Solved Example"
ECE by MVK
9. "📘 Marginal Distribution Functions from Joint PDF | Probability & Statistics Solved Example"
13:56
14. First and Second order moments from Joint PDF
ECE by MVK
14. First and Second order moments from Joint PDF
21:44
6. Mean, Mean Square & Variance from Characteristic Function | Probability & Random Processes
ECE by MVK
6. Mean, Mean Square & Variance from Characteristic Function | Probability & Random Processes
13:34
Exponential Random Variable | PDF and CDF Explained | Probability & Statistics
ECE by MVK
Exponential Random Variable | PDF and CDF Explained | Probability & Statistics
8:14
Axioms of Probability | Probability & Statistics Made Easy | Exam Preparation
ECE by MVK
Axioms of Probability | Probability & Statistics Made Easy | Exam Preparation
5:24
2. Mean, Variance & Mean Square of a Continuous Random Variable | Probability & Statistics Example"
ECE by MVK
2. Mean, Variance & Mean Square of a Continuous Random Variable | Probability & Statistics Example"
9:48
Mean, Variance & Mean Square of a Discrete Random Variable | Probability & Statistics Made Easy"
ECE by MVK
Mean, Variance & Mean Square of a Discrete Random Variable | Probability & Statistics Made Easy"
10:32
Finding the constant k and distribution function of random variable from the given Density Function
ECE by MVK
Finding the constant k and distribution function of random variable from the given Density Function
6:39
"PDF to CDF Conversion & Probability Solved | Feb 2022 Question | Easy Explanation!" UNIT1
ECE by MVK
"PDF to CDF Conversion & Probability Solved | Feb 2022 Question | Easy Explanation!" UNIT1
13:33
This CDF Problem is Easier Than You Think Finding the Constant 'k' in a CDF | Explained Simply UNIT1
ECE by MVK
This CDF Problem is Easier Than You Think Finding the Constant 'k' in a CDF | Explained Simply UNIT1
6:32
Example with Distribution Function Plotting CDF and find Probability values in a given range -UNIT1
ECE by MVK
Example with Distribution Function Plotting CDF and find Probability values in a given range -UNIT1
15:03
Problem solving on Total probability Theorem
ECE by MVK
Problem solving on Total probability Theorem
8:21
Given β=50 & Collector current of 10mA, determine the value of Emitter Current & α of the Transistor
ECE by MVK
Given β=50 & Collector current of 10mA, determine the value of Emitter Current & α of the Transistor
4:44
Finding the value of VGS &Trans conductance from the given drain current, saturation current & Vp
ECE by MVK
Finding the value of VGS &Trans conductance from the given drain current, saturation current & Vp
6:04
Perceptron versus McCulloch-Pitts model in Artificial Neural Networks
ECE by MVK
Perceptron versus McCulloch-Pitts model in Artificial Neural Networks
13:53
McCulloch-Pitts Model to implement logical AND operation || ANN
ECE by MVK
McCulloch-Pitts Model to implement logical AND operation || ANN
3:48
McCulloch Pitts Neural Model || M-P's Neural Model
ECE by MVK
McCulloch Pitts Neural Model || M-P's Neural Model
14:16
Convolution between r(t) and e^-2t u(t) @vinu430
ECE by MVK
Convolution between r(t) and e^-2t u(t) @vinu430
13:07
Convolution between u(t+3) and e^-3t u(t)
ECE by MVK
Convolution between u(t+3) and e^-3t u(t)
8:24
Convolution between x(t)=e^(-4t)u(t)and h(t)= e^(-2t)u(t) by using Fourier Transform Property
ECE by MVK
Convolution between x(t)=e^(-4t)u(t)and h(t)= e^(-2t)u(t) by using Fourier Transform Property
12:45
Convolution between x(t) = e-4t u(t) and h(t) = e-2t u(t)
ECE by MVK
Convolution between x(t) = e-4t u(t) and h(t) = e-2t u(t)
12:51
Bandwidth of Operational Amplifier in Noninverting Mode
ECE by MVK
Bandwidth of Operational Amplifier in Noninverting Mode
10:13
Input and Output Resistance Expression of Op-AMP in Inverting mode
ECE by MVK
Input and Output Resistance Expression of Op-AMP in Inverting mode
9:57
Inverting Amplifier Gain Derivation for an Ideal Op Amp
ECE by MVK
Inverting Amplifier Gain Derivation for an Ideal Op Amp
9:14
Closed-Loop Gain of Voltage-Shunt Feedback Configuration of Op-Amp / Inverting Amplifier
ECE by MVK
Closed-Loop Gain of Voltage-Shunt Feedback Configuration of Op-Amp / Inverting Amplifier
9:35
Output Resistance of Non-Inverting Amplifier
ECE by MVK
Output Resistance of Non-Inverting Amplifier
9:40
Input Resistance of Operational Amplifier in Non Inverting Mode
ECE by MVK
Input Resistance of Operational Amplifier in Non Inverting Mode
8:36
Expression of voltage gain of the Non-Inverting Amplifier for an Ideal Op-Amp
ECE by MVK
Expression of voltage gain of the Non-Inverting Amplifier for an Ideal Op-Amp
4:42
Closed-Loop Gain of Non-Inverting Amplifier / Op-AMP in Voltage Series Feedback Configuration
ECE by MVK
Closed-Loop Gain of Non-Inverting Amplifier / Op-AMP in Voltage Series Feedback Configuration
17:19
Operational amplifier in closed loop configuration
ECE by MVK
Operational amplifier in closed loop configuration
31:10
Operational Amplifier Open Loop Configurations
ECE by MVK
Operational Amplifier Open Loop Configurations
19:24
Introduction to Decoder and its it operation
ECE by MVK
Introduction to Decoder and its it operation
23:16
Multiplexer operation and Boolean Function Implementation
ECE by MVK
Multiplexer operation and Boolean Function Implementation
43:20
Implementation of given Boolean Function by using Multiplexer
ECE by MVK
Implementation of given Boolean Function by using Multiplexer
23:12
Electrical Characteristics of Operational Amplifiers
ECE by MVK
Electrical Characteristics of Operational Amplifiers
37:34
Classification of Differential Amplifiers in Operational Amplifiers
ECE by MVK
Classification of Differential Amplifiers in Operational Amplifiers
11:29
Obtaining Product of Sum Expression using Karanaugh Map
ECE by MVK
Obtaining Product of Sum Expression using Karanaugh Map
26:12
Gray to Binary Code Converter
ECE by MVK
Gray to Binary Code Converter
25:14
Operational Amplifier- Ideal and Practical Characteristics
ECE by MVK
Operational Amplifier- Ideal and Practical Characteristics
48:01
Half Subtractor Full Subtractor
ECE by MVK
Half Subtractor Full Subtractor
26:58
Introduction to Combinational Circuits-Half Adder and Full Adder
ECE by MVK
Introduction to Combinational Circuits-Half Adder and Full Adder
55:03