Bindeshwar S. Kushwaha, Author at PostNetwork Academy

Expectation of a Continuous Random Variable: Uniformly Distributed Random Variable

Leave a Comment / Probability and Statistics / Bindeshwar S. Kushwaha

Expectation of a Continuous Random Variable Expectation of a continuous random variable is defined as Suppose a continuous random variable X is uniformly distributed on [a, b]. Density function of uniformly distributed random variable X is Expectation of uniformly distributed random variable X is See Video See Also: Expectation in Statistics Expectation of a Discrete […]

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Definition of Probability

Leave a Comment / Probability and Statistics / Bindeshwar S. Kushwaha

Mathematical or Classical Probability- If a random experiment is conducted which results in n exhaustive, mutually exclusive, and equally likely outcomes and there are m favorable cases of occurrence of an event E. Then probability of happening event E is P(E)=m/n Suppose you are tossing a coin and want to get probability of getting head

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Joint and Marginal Probability Mass Function

Leave a Comment / Maths, Probability and Statistics / Bindeshwar S. Kushwaha

Joint and Marginal Probability Mass Function For UploadingIf (X,Y) is a two-dimensional discrete random variable, then joint probability mass function of X and Y denoted by pxy and is defined as pxy(xi,yj)=P(X=xi,Y=yj) If you toss three coins the following sample space you will get. S={TTT, TTH, THT, THH, HTT, HTH, HHT,HHH} X—- Occurrence of heads

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Terminologies Related to Probability Theory

Leave a Comment / Uncategorized / Bindeshwar S. Kushwaha

1-Random Experiment- If an experiment is conducted then the outcome will not be unique but it may be from possible list of outcomes. Example- If you are tossing two coins the possible outcomes would be {TT, TH, HT,HH}. 2- Events- Outcome of a random experiment is called events, if you are tossing two coins then

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Entropy: A Measure of Uncertainty

Leave a Comment / Information Theory, Probability and Statistics / Bindeshwar S. Kushwaha

Entropy of a random variable measures uncertainty inherent to possible outcomes of the variable. H(X)= -Σin pilog2pi , where i=1, 2, 3 , 4, …..,n Where pi are probabilities of associated events. Take an example of tossing two coins, then you will have sample space . S={T,H} And X is defined as number of

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Expectation of a Discrete Random Variable

Leave a Comment / Probability and Statistics / Bindeshwar S. Kushwaha

A random variable X is a function from a sample S to real numbers R, i.e X: S——>R. Suppose you are tossing three coins then the sample space would be. S={HHH,TTH, THT, THH, HTT, HTH, HHT, HHH} Then X maps sample space to real numbers. Where you can see that R={0,1,2, 3} You can also

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Random-Variables-and-Probability-Density-Function

Random Variable and Probability Density Function

Leave a Comment / Maths, Probability and Statistics / Bindeshwar S. Kushwaha

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Taylor’s and Maclaurin’s Series

Leave a Comment / Maths / Bindeshwar S. Kushwaha

Taylor’s theorem states that we can get expansion of a function f(x) at x=a This can be written as When we put a=0 it becomes Maclaurin’s series, and it has a lot of applications in mathematics and other areas. If you want expansion of ex you will require to find f(0), f’(0), f’’(0), f’’’(0), ………….,

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Servo Motor with Arduino

Leave a Comment / Internet of Things / Bindeshwar S. Kushwaha

In this post, I am going to explain how to attach a Servo motor with Arduino. I will explain about programming and how to connect Servo motor with Arduino. You will require to install Servo library for functioning of Servo motor #include <Servo.h> Servo s; int i = 0; void setup() { s.attach(9); } void

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Train a Simple Deep Learning Network using Keras and Iris DataSet

Leave a Comment / Deep Learning, Machine Learning / Bindeshwar S. Kushwaha

Keras Deep learning framwork is very popular among researchers and developers which is built at top of Tensorflow. Furthermore, Keras is very handy and easy to use, creating layers and connections is matter of few lines of code. In this post, I am going to explain, how you can train a simple Deep learning network

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Bindeshwar S. Kushwaha

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