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4 votes
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Are Bayesian networks important to learn in 2018?

*AI, A Modern Approach," was given that title to break from previously narrow approaches to duplicating desirable qualities of human thinking. Although Bayesian networks require somewhat resource ...
Douglas Daseeco's user avatar
3 votes

What is the definition of a heuristic function in the BayesChess paper?

By far the most common form of heuristic evaluation functions for Chess-playing (or, really, any game-playing) agents are simple linear functions. At least when we're talking about handcrafted ...
Dennis Soemers's user avatar
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2 votes

What is the definition of a heuristic function in the BayesChess paper?

Heuristics can be understood aas rules. Typically heuristics are thought of as problem-specific strategies. Expert systems were an early form of AI that utilized rules-based decisions. In a game-...
DukeZhou's user avatar
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2 votes
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What does all the formula and pictures mean?

According to the provided article, $$ \begin{equation} \tag{1} P(D| {\mathcal{E}};\ \theta ) \end{equation} $$ is a probability of disease $D$ given findings $\mathcal{E}$, and a model $\theta$ that ...
Aray Karjauv's user avatar
2 votes

Problem with Proposition 1 of Google Deepmind's 'Weight uncertainty in Neural Networks'

we start with $\frac{\partial}{\partial \theta} \mathbb E_{q(\mathbf w\mid\theta)}[f(\mathbf w, \theta)]$ using definition of expectation for continuous case: $\mathbb E[X] = \int xp(x) dx$ ...
Brale's user avatar
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1 vote
Accepted

What is the difference between an input and observed data in a Bayesian neural network?

You're pretty close: $x,y$ correspond to a single data point $\mathcal{D}$ is the whole dataset Given this, you can read the posterior $p(y|x,D)$ as " what's the distribution of $y$ given that ...
Alberto's user avatar
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1 vote

What are the main benefits of using Bayesian networks?

Yes, you are correct that one of the key benefits of Bayesian networks is that they allow you to calculate joint probability distributions without directly using the chain rule of probability. ...
Hans-Peter Schrei's user avatar
1 vote

What is the difference between a Bayesian Network and a Markov Chain?

The main difference between a Bayesian network and a Markov chain is not that a Markov Chain is not directional, it is that the graph of the Bayesian network is not trivial whereas the graph of a ...
David's user avatar
  • 4,910
1 vote

Why do Bayesian algorithms work well with small datasets?

The main reason should be that Bayesian algorithms naturally incorporate a form of regularisation (the prior), so they should be less prone to over-fitting the small dataset. Of course, the choice of ...
nbro's user avatar
  • 40.6k
1 vote

How do I determine the most appropriate classifier for a certain problem?

This is one of the main skills that separates someone with a deep understanding of, and experience in, machine learning learning, from a neophyte. There are several approaches: Try several methods, ...
John Doucette's user avatar
1 vote

Is it possible to compute $P( F \mid S )$ given $P(F \mid S,A)$ and $P(F \mid S, \lnot A)$ in Bayesian network?

This is a bit of a puzzle but you can compute a reasonable narrow limit even without knowing whether or not $P(S,A) = P(S) P(A)$. Start with the contingency table relating $P(S, A)$, $P(S,\neg A)$, $...
Sextus Empiricus's user avatar
1 vote

Why is exact inference in a Bayesian network both NP-hard and P-hard?

It's not completely clear from your question, but it looks like you want to prove that exact inference in a Bayesian Network is both NP-Hard and P-Hard. It appears that you have proven that it is NP-...
John Doucette's user avatar
1 vote

Are Bayesian networks important to learn in 2018?

The chapters for Bayesian Networks are: Quantifying Uncertainty Probabilistic Reasoning Dynamic Bayesian don't forget: Naive Bayes, hidden variables, Markov Maybe helpful: . Are We Going in the ...
PrivateStatic's user avatar

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