frozenca / ml-murphy Goto Github PK
View Code? Open in Web Editor NEWComplete solutions for exercises and MATLAB example codes for "Machine Learning: A Probabilistic Perspective" 1/e by K. Murphy
Complete solutions for exercises and MATLAB example codes for "Machine Learning: A Probabilistic Perspective" 1/e by K. Murphy
I just worked out question 3.18 myself, and found an answer of 9.31. Which differs by one decimal position from the answer in this repo.
I found this other solution manual here that like me also arrived a 9.31:
https://github.com/ArthurZC23/Machine-Learning-A-Probabilistic-Perspective-Solutions/blob/master/3/18.pdf
I think the mistake here is in the step where the uniform pmf gets plugged in, resulting in a P(D | M_1) = 1/(N+1).
Assuming that M_1 here refers to the alternative hypothesis H1, this doesn't seem right.
This seems like it should be:
P(D | H_1) = \int_{0}^{1} L(\theta | D) P(\theta | H_1)
With L(\theta | D) the likelihood function and P(\theta | H_1) the uniform prior on \theta as specified in the alternative hypothesis.
Calculating that integral, we find a denominator of 110 rather than 11.
In the numerator, strictly speaking this integral exists there too:
P(D | H_0) = \int_{0}^{1} L(\theta | D) P(\theta | H_0)
However, in the numerator that doesn't matter because P(\theta | H_0) has a Dirac point mass on 0.5, and thus the integral drops off and we are left with only the Bernoulli likelihood function.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.