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7 votes

What is the relationship between fuzzy logic and objective bayesian probability?

This was a somewhat hotly debated question in the 1980s. The debate was more-or-less ended with papers like Cheeseman's In Defense of Probability. The short answer is that Fuzzy Logic does not just ...
John Doucette's user avatar
4 votes

How does the Dempster-Shafer theory differ from Bayesian reasoning?

Demster-Shafer Theory and Bayesian Networks were both techniques that rose to prominence within AI in the 1970's and 1980's, as AI started to seriously grapple with uncertainty in the world, and move ...
John Doucette's user avatar
3 votes
Accepted

How does maximum approximation of the posterior choose a distribution?

Introduction: MAP finds a point estimate! As opposed to your apparently current belief, in maximum a posteriori (MAP) estimation, you are looking for a point estimate (a number or vector) rather than ...
nbro's user avatar
  • 41k
1 vote
Accepted

Methods for sequential decision optimization problem with nonlinear bayesian reward function

One possible alternative approach is incorporating deep reinforcement learning (DRL) techniques. These techniques are sequential since they incorporate lookahead, and they are designed to attempt to ...
DeepQZero's user avatar
  • 1,494
1 vote
Accepted

Why is the E step in expectation maximisation algorithm called so?

In expectation step, firstly we calculate the posterior of latent variable $Z$ and then the $Q(θ | θ^{(t)})$ is defined as the expected value of the log likelihood of $θ$, with respect to the current ...
ddaedalus's user avatar
  • 929
1 vote
Accepted

Understanding how to calculate $P(x|c_k)$ for the Bernoulli naïve Bayes classifier

Bernoulli naïve Bayes $P(x \mid c_k) = \prod^{n}_{i=1} p^{x_i}_{ki} (1-p_{ki})^{(1-x_i)}$ Let's examine the example of document classification. Let K different text classes and n different terms that ...
ddaedalus's user avatar
  • 929

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