User calveeen

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4

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919

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Why is the normalisation constant in Bayesian not a marginal distribution

Aug 5 at 8:29 Sextus Empiricus 29.8k

3

votes

1

answer

22

views

Dynamic bayesian model conditional independence

machine-learning probability self-study graphical-model bayesian-network

Jan 1 at 11:51 gunes 39.1k

3

votes

1

answer

61

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Bayesian mixture model joint posterior

bayesian gaussian-mixture bayesian-network gibbs infinite-mixture-model

Oct 18 at 16:55 mef 2,216

2

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1

answer

11

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Sparse and variational Gaussian Process lower bound

machine-learning bayesian gaussian-process variational-bayes

11h ago user20160 25.4k

2

votes

1

answer

146

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Why do we do Bayesian Inference about function parameters?

self-study bayesian posterior

Oct 13 at 6:40 Tim♦ 88.4k

2

votes

1

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24

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SARSA when policy is not epilon greedy

reinforcement-learning q-learning sarsa

Oct 9 at 22:55 Neil Slater 5,221

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0

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35

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Intuition on why Gibbs Sampling samples from the posterior distribution

sampling mcmc posterior intuition gibbs

Aug 20 at 3:43 kjetil b halvorsen♦ 51.9k

1

vote

1

answer

43

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Loss function for regression

regression self-study joint-distribution

Dec 2 at 10:48 Lewian 4,228

1

vote

2

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36

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Why does greedy algorithm for Multi-arm bandit incur linear regret?

reinforcement-learning multiarmed-bandit

Nov 23 at 23:38 Julien S. 111

1

vote

0

answers

7

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Why is least squares temporal difference (LSTD) method more sample efficient compared to Temporal difference (TD) for value function approximation

reinforcement-learning temporal-difference

Sep 23 at 10:18 calveeen 435

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0

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14

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Marginal Cluster assignments for Dirichlet Process mixture model

conditional-probability dirichlet-distribution dirichlet-process

Aug 21 at 6:48 calveeen 435

1

vote

1

answer

109

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Bayes' theorem in Gaussian mixture model for $p(z_i = k | x_i, \mu_k,\Sigma_k)$

probability bayesian conditional-probability gaussian-mixture

Aug 6 at 14:28 Xi'an 78.4k

0

votes

0

answers

10

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Why does using conditional random field avoid independence assumption

classification naive-bayes graphical-model generative-models conditional-random-field

Jan 5 at 7:54 calveeen 435

0

votes

0

answers

17

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Expected Improvement formula for Bayesian Optimisation

machine-learning probability gaussian-process bayesian-optimization

Dec 28 at 3:51 calveeen 435

0

votes

2

answers

59

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Generative story for a variational auto encoder

machine-learning neural-networks bayesian autoencoders variational-bayes

Dec 25 at 14:53 stbv 126

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votes

1

answer

34

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how does the posterior predictive distribution account for differences in uncertainty?

probability neural-networks bayesian uncertainty

Dec 11 at 6:16 statian 278

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votes

1

answer

37

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$p(x|\theta)$ in predictive distribution vs likelihood function in bayes

bayesian likelihood posterior

Dec 9 at 10:33 Fiodor1234 575

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votes

1

answer

23

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Bayes by backprop unbiased monte carlo gradients

bayesian bayesian-network variational-bayes

Dec 7 at 2:03 calveeen 435

0

votes

1

answer

21

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Minimising KL divergence between two distributions

self-study maximum-likelihood entropy information-theory kullback-leibler

Dec 2 at 8:44 Davide ND 1,918

0

votes

0

answers

7

views

Encoding prior knowledge in image classification (A bayesian approach ?)

bayesian classification inference image-processing

Nov 30 at 5:18 calveeen 435

0

votes

1

answer

25

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policy gradient sampling stationary state distribution

distributions reinforcement-learning policy-gradient

Nov 2 at 15:28 shimao 17.1k

0

votes

0

answers

11

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Is the product of conditional posterior equal to the joint distribution?

conditional-probability posterior joint-distribution

Oct 17 at 7:00 calveeen 435

0

votes

0

answers

10

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Measure of base distribution $G_0(A)$ in the Dirichlet process

machine-learning dirichlet-distribution dirichlet-process nonparametric-bayes

Sep 17 at 14:39 calveeen 435

0

votes

0

answers

7

views

Dirichlet process Posterior notation

machine-learning dirichlet-distribution dirichlet-process nonparametric-bayes

Sep 17 at 14:37 calveeen 435

1

0

votes

1

answer

76

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Metropolis Hastings algorithm for joint posterior of probability of heads for 2 coins

bayesian mcmc posterior metropolis-hastings

Sep 13 at 7:55 calveeen 435

0

votes

1

answer

21

views

What is the Dirichlet Proces Mixture Models posterior

posterior dirichlet-process nonparametric-bayes

Aug 18 at 7:24 Mikkel N. Schmidt 377

0

votes

2

answers

70

views

Is the density of the unnormalised posterior distribution the same as the density for normalised posterior? [duplicate]

probability bayesian posterior

Aug 11 at 16:20 carlo 3,736

1

0

votes

1

answer

52

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Why can't we use Monte carlo approximation for normalising constant in Baye's theorem

probability bayesian normalizing-constant

Aug 11 at 7:25 Xi'an 78.4k

0

votes

1

answer

49

views

Is the product of conditional probability distributions a valid distribution?

bayesian conditional-probability

Aug 8 at 20:38 Sycorax♦ 64.5k

0

votes

1

answer

28

views

Gaussian Mixture Model $p(x_i | z_i = k)$ a likelihood or probability?

continuous-data gaussian-mixture marginal

Aug 6 at 7:27 Xi'an 78.4k

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32 Questions

Why is the normalisation constant in Bayesian not a marginal distribution

Dynamic bayesian model conditional independence

Bayesian mixture model joint posterior

Sparse and variational Gaussian Process lower bound

Why do we do Bayesian Inference about function parameters?

SARSA when policy is not epilon greedy

Intuition on why Gibbs Sampling samples from the posterior distribution

Loss function for regression

Why does greedy algorithm for Multi-arm bandit incur linear regret?

Why is least squares temporal difference (LSTD) method more sample efficient compared to Temporal difference (TD) for value function approximation

Marginal Cluster assignments for Dirichlet Process mixture model

Bayes' theorem in Gaussian mixture model for $p(z_i = k | x_i, \mu_k,\Sigma_k)$

Why does using conditional random field avoid independence assumption

Expected Improvement formula for Bayesian Optimisation

Generative story for a variational auto encoder

how does the posterior predictive distribution account for differences in uncertainty?

$p(x|\theta)$ in predictive distribution vs likelihood function in bayes

Bayes by backprop unbiased monte carlo gradients

Minimising KL divergence between two distributions

Encoding prior knowledge in image classification (A bayesian approach ?)

policy gradient sampling stationary state distribution

Is the product of conditional posterior equal to the joint distribution?

Measure of base distribution $G_0(A)$ in the Dirichlet process

Dirichlet process Posterior notation

Metropolis Hastings algorithm for joint posterior of probability of heads for 2 coins

What is the Dirichlet Proces Mixture Models posterior

Is the density of the unnormalised posterior distribution the same as the density for normalised posterior? [duplicate]

Why can't we use Monte carlo approximation for normalising constant in Baye's theorem

Is the product of conditional probability distributions a valid distribution?

Gaussian Mixture Model $p(x_i | z_i = k)$ a likelihood or probability?