RS Aggarwal Class 12 Bayes Theorem and its Applications PDF
Welcome to sidclasses.in. Here we have provided RS Aggarwal Class 12 Bayes Theorem and its Applications PDF which you can download very easily. Rs Aggarwal is a mathematics book based on NCERT with lots of questions to practice. If you have cleared your concept from NCERT maths book class 12 then now it’s time for you to go for the question of RS Aggarwal Class 12 Bayes Theorem and its Applications PDF.
What are Bayes Theorem and its Applications?
Bayes’ theorem is a fundamental concept in probability theory that describes the relationship between the probability of an event occurring and the probability of another event occurring in relation to that event. It is named after Reverend Thomas Bayes, an 18th-century statistician, and theologian who first proposed it.
The theorem can be written in the following form: P(A|B) = P(B|A) P(A) / P(B)
Where:
- P(A|B) is the probability of event A occurring given that event B has occurred (also known as the posterior probability)
- P(B|A) is the probability of event B occurring given that event A has occurred (also known as the likelihood)
- P(A) is the prior probability of event A occurring
- P(B) is the prior probability of event B occurring
Bayes’ theorem allows us to update our beliefs about the probability of an event occurring based on new information. It is particularly useful in decision-making and problem-solving when we have uncertain or incomplete information.
The theorem has a wide range of applications in fields such as statistics, machine learning, natural language processing, medical diagnosis, image recognition, and more. In machine learning, it is used in Bayesian learning algorithms and Bayesian Networks. Natural Language Processing, it is used in text classification, spam filtering, and information retrieval. In medical diagnostics, it is used to calculate the likelihood of a disease based on symptoms and test results.
Ch-30-Bayess-Theorem-and-its-ApplicationsRS Aggarwal Class 12 (All Chapters PDF)
| Chapter no. | chapter name | |
|---|---|---|
| 1 | Relations | Download |
| 2 | Functions | Download |
| 3 | Binary Operations | Download |
| 4 | Inverse Trigonometric Functions | Download |
| 5 | Matrices | Download |
| 6 | Determinants | Download |
| 7 | Adjoint and Inverse Of Matrix | Download |
| 8 | System of Linear Equations | Download |
| 9 | Continuity and Differentiability | Download |
| 10 | Differentiations | Download |
| 11 | Applications of Derivatives | Download |
| 12 | Indefinite Integrals | Download |
| 13 | Methods of integration | Download |
| 14 | Some Special integrals | Download |
| 15 | Integration using Partial Functions | Download |
| 16 | Definite integrals | Download |
| 17 | Area of Bounded Regions | Download |
| 18 | Differential equations and their formations | Download |
| 19 | Differential equations with variable separable | Download |
| 20 | Homogeneous Integral equations | Download |
| 21 | Linear Differential equations | Download |
| 22 | Vectors And Their Properties | Download |
| 23 | Scalar, or Dot product of Vectors | Download |
| 24 | Cross or vector products of Vector | Download |
| 25 | Product of three vectors | Download |
| 26 | Fundamental Concepts of 3D Geometry | Download |
| 27 | Straight Line in Space | Download |
| 28 | The Plane | Download |
| 29 | Probability | Download |
| 30 | Bayes’s Theorem and its Applications | Download |
| 31 | Probability Distribution | Download |
| 32 | Binomial Distributions | Download |
| 33 | Linear Programming | Download |