Bayesian methods for hackers : (Record no. 39740)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01953nam a22001697a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190424b2018 xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789353063641 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 DAV-C |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Davidson-Pilon, Cameron |
| 245 ## - TITLE STATEMENT | |
| Title | Bayesian methods for hackers : |
| Remainder of title | probabilistic programming and Bayesian inference / |
| Statement of responsibility, etc. | Cameron Davidson-Pilon |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Pearson |
| Date of publication, distribution, etc. | 2018 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 226 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 399.00. |
| 500 ## - GENERAL NOTE | |
| General note | The next generation of problems will not have deterministic solutions the solutions will be statistical that rely on mountains or mounds, of data. Bayesian methods offer a very flexible and extendible framework to solve these types of problems. For programming students with minimal background in mathematics, this example-heavy guide emphasizes the New technologies that have allowed the inference to be abstracted from complicated underlying mathematics. Using Bayesian Methods for Hackers, students can start leveraging powerful Bayesian tools right now gradually deepening their theoretical knowledge while already achieving powerful results in areas ranging from marketing to finance. Students will master Bayesian techniques that will play an increasingly crucial role in every data scientist's toolkit<br/>Shows students how to solve statistically-based problems relying on mountains of data<br/>Teaches through realistic (non-toy) examples built with the Python PyMC library, including start-to-finish application case studies<br/>Gives an intuitive understanding of key concepts such as clustering, convergence, autocorrelation and thinning Chapter 1: the Philosophy of Bayesian Inference<br/>Chapter 2: A Little More on PyMC<br/>Chapter 3: Opening the Black Box of MCMC<br/>Chapter 4: the Greatest Theorem Never Told<br/>Chapter 5: the Greatest Theorem Never Told<br/>Chapter 6: Getting Our Priorities Straight<br/>Chapter 7: Bayesian A/B Testing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Soft computing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Bayesian statistical decision theory |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 24/04/2019 | 399.00 | 2 | 006.3 DAV-C | 38246 | 20/02/2025 | 24/01/2025 | 24/04/2019 | Books |