확률이론-전통적인 확률 모델(Probability Model)
2018-11-13
- 정리
- Introduction a Generative Model(생성모델)
- 이항분포(Binomial Distribution)
- 베르누이분포
- 정규분포(Normal Distribution)
- 가우시안분포
- 다항분포(Multinomial Distribution)
- 베르누이시행/베르누이독립시행
- Likelihood와 Probability의 차이
- Likelihood(가능도)
- Log-likelihood
- Estimator 종류
- XX-Likelihood Estimation(가능도 추정량)
- MLE(Maximum Likelihood Estimation)
- Discriminative Approach & Generative Approach
- GDA(Gaussian Discriminant Analysis)
- GMM(Gaussian Mixture Model) = MoG(Mixture of Gaussian)
- EM(Expectation Maximization)
- Residual
- Cost=Error=오차=손실=비용
- Cross entropy error, CEE (교차 엔트로피 오차)
- Linear Regression
- Logistic Regression(Binary Classification) vs Multinomial Logistic Regression = Softmax(Multinomial Classification)
- Naïve Bayes
- Gaussian Discriminant Analysis
- Joint Likelihood
- Joint Probability
- Analytic solution
- 모수(Parameters)
- Bayes’ Rule
- Expectation
- Lower Bound of Log-likelihood
- Lower Bound of Log-likelihood = Log-likelihood 함수의 평균(기대값)을 의미?
- Jensen’s Inequality
- Posterior Probability(사후확률) / Prior Probability(사전확률)
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#container {
float: left;
margin: 0 -240px 0 0;
width: 100%;
}
<nav class="pagination" role="navigation">
{% if page.previous %}
<a href="{{ site.url }}{{ page.previous.url }}" class="btn" title="{{ page.previous.title }}">Previous article</a>
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module Jekyll
class TagIndex < Page
def initialize(site, base, dir, tag)
@site = site
@base = base
@dir = dir
@name = 'index.html'
self.process(@name)
self.read_yaml(File.join(base, '_layouts'), 'tag_index.html')
self.data['tag'] = tag
tag_title_prefix = site.config['tag_title_prefix'] || 'Tagged: '
tag_title_suffix = site.config['tag_title_suffix'] || '–'
self.data['title'] = "#{tag_title_prefix}#{tag}"
self.data['description'] = "An archive of posts tagged #{tag}."
end
end
end
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{% endif %}
</nav><!-- /.pagination -->
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