This section was developed by following Rudin, Principles of Mathematical Analysis, Chapter 2.
Connected Sets
Two subsets and of a metric space are said to be separated if both and are empty, i.e., if no point of lies in the closure of and no point of lies in the closure of
Referenced by (2 direct, 157 transitive)
Direct references:
Transitive (depth 1):
- connected-sets-in-r1-are-intervals
- Domain
- proof-of-open-set-in-r1-is-countable-union-of-disjoint-segments
Transitive (depth 2):
- Cauchy Integral Theorem
- Cauchy's Integral Theorem
- closed-path-of-path-independent-integral-is-zero
- Contour Integral
- Divergence Theorem of Gauss
- Green's Theorem
- Parameter Space
- Path Independent
- Random Variable
- remark-12
- Stoke's Theorem
- Taylor Expansion Theorem
- theorem-19
- Volume Integral
- Well-posedness of IVPs
Transitive (depth 3):
- proof-of-cauchys-integral-theorem
- proof-of-integral-of-one-over-z-around-unit-circle
- Parametric Family
- fundamental-theorem-of-line-integrals
- remark-10
- remark-11
- theorem-7-intuition
- A/B Test
- Absolutely Continuous Random Variable
- Binomial Distribution
- Central Limit Theorem
- chebyshev-fish
- Chebyshev's Inequality
- chebyshevs-inequality-note
- Conditional Entropy
- Conditional Independence Given Intermediate Step
- Conditional Mutual Information
- Convolution
- corollary-88
- Covariance
- Cumulative Distribution Function
- Discrete Random Variable
- Entropy
- Expected Value
- Function of middle variable forms Markov Chain
- Identically Distributed
- Independent and Identically Distributed
- Jensen's Inequality
- Joint Entropy
- joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- Joint Probability Distribution
- Law of Large Numbers
- law-of-large-numbers-note
- Markov Chain
- markov-inequality-fish
- Markov's Inequality
- Continuity Theorem for Moment Generating Functions
- Moment Generating Function of a Sum
- Moment Generating Function
- Mutual Independence
- Mutual Information
- Negative Binomial Distribution
- Nonnegativity of Mutual Information
- Normal Approximation to the Binomial
- Normal Distribution
- Pairwise Independence
- Poisson Approximation to the Binomial
- Poisson Distribution
- Probability Distribution
- proof-of-chebyshevs-inequality
- proof-of-fanos-inequality
- proof-of-law-of-large-numbers
- remark-14
- Reversibility of Markov Chains
- Self-Information
- Standard Normal Distribution
- State Space
- Sum of Independent Normal Random Variables
- Sum of Independent Poisson Random Variables
- theorem-16
- theorem-7
- Uniform Distribution
- Uniform distribution maximizes entropy
- Variance
Transitive (depth 4):
- Probability Density Function
- Hypergeometric Distribution
- note-25
- proof-of-chain-rule-for-mutual-information
- Geometric Distribution
- Probability of Error
- Conditioning Reduces Entropy
- Cross-Entropy
- doubly-stochastic-maps-increase-entropy-intuition
- Entropy Rate
- fanos-inequality-remark
- Gibbs' Inequality
- Chain rule for joint entropy
- KL Divergence
- lemma-93
- Noiseless channel transmitting discrete symbols
- note-8
- proof-of-concavity-of-entropy
- proof-of-doubly-stochastic-maps-increase-entropy
- proof-of-joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- proof-of-uniform-distribution-maximizes-entropy
- theorem-12
- Weak Asymptotic Equipartition Property
- Interpretation
- markovs-inequality-note
- proof-of-markovs-inequality
- proof-of-weak-asymptotic-equipartition-property
- proof-of-processing-cannot-increase-information
- proof-of-corollary-95
- proof-of-gibbs-inequality
- proof-of-lemma-93
- proof-of-log-sum-inequality
- proof-of-conditioning-reduces-entropy
- Conditional Relative Entropy
- Conditionally Independent
- proof-of-concavity-and-convexity-of-mutual-information
- Ergodic
- note-19
- proof-of-characterization-of-sufficiency
- proof-of-data-processing-inequality
- Stationary Distribution of an Ergodic Chain
- Strong Asymptotic Equipartition Property (Shannon--McMillan--Breiman)
- Sufficient Statistic
- Concavity and Convexity of Mutual Information
- proof-of-nonnegativity-of-mutual-information
- Bias
- Characterization of Sufficiency
- Conditional Probability Distribution
- Convexity of Relative Entropy
- Product Distribution
- proof-of-convexity-of-relative-entropy
- proof-of-nonnegativity-of-relative-entropy
- note-15
- remark-8
- note-3
- concavity-of-entropy-intuition
- Confidence Interval for a Mean, Known Variance
- Confidence Interval for a Mean, Unknown Variance
- Standard Deviation
- variance-interpretation
Transitive (depth 5):
- proof-of-joint-is-marginal-times-conditional
- proof-of-chain-rule-for-relative-entropy
- proof-of-nonnegativity-of-conditional-relative-entropy
- Nonnegativity of Conditional Mutual Information
- proof-of-conditional-independence-given-intermediate-step
- proof-of-nonnegativity-of-conditional-mutual-information
- Model Training
- proof-of-chain-rule-for-entropy
- proof-of-mutual-information-and-entropy
- convexity-of-relative-entropy-intuition
- remark-32
- Multiplication Rule for Distributions
- proof-of-corollary-77
- characterization-of-sufficiency-example
- Minimal Sufficient Statistic
If is a metric space, a set is said to be connected if is not a union of two nonempty separated sets.
Referenced by (4 direct, 154 transitive)
Direct references:
Transitive (depth 1):
- Cauchy Integral Theorem
- Cauchy's Integral Theorem
- closed-path-of-path-independent-integral-is-zero
- Contour Integral
- Divergence Theorem of Gauss
- Green's Theorem
- Parameter Space
- Path Independent
- Random Variable
- remark-12
- Stoke's Theorem
- Taylor Expansion Theorem
- theorem-19
- Volume Integral
- Well-posedness of IVPs
Transitive (depth 2):
- proof-of-cauchys-integral-theorem
- proof-of-integral-of-one-over-z-around-unit-circle
- Parametric Family
- fundamental-theorem-of-line-integrals
- remark-10
- remark-11
- theorem-7-intuition
- A/B Test
- Absolutely Continuous Random Variable
- Binomial Distribution
- Central Limit Theorem
- chebyshev-fish
- Chebyshev's Inequality
- chebyshevs-inequality-note
- Conditional Entropy
- Conditional Independence Given Intermediate Step
- Conditional Mutual Information
- Convolution
- corollary-88
- Covariance
- Cumulative Distribution Function
- Discrete Random Variable
- Entropy
- Expected Value
- Function of middle variable forms Markov Chain
- Identically Distributed
- Independent and Identically Distributed
- Jensen's Inequality
- Joint Entropy
- joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- Joint Probability Distribution
- Law of Large Numbers
- law-of-large-numbers-note
- Markov Chain
- markov-inequality-fish
- Markov's Inequality
- Continuity Theorem for Moment Generating Functions
- Moment Generating Function of a Sum
- Moment Generating Function
- Mutual Independence
- Mutual Information
- Negative Binomial Distribution
- Nonnegativity of Mutual Information
- Normal Approximation to the Binomial
- Normal Distribution
- Pairwise Independence
- Poisson Approximation to the Binomial
- Poisson Distribution
- Probability Distribution
- proof-of-chebyshevs-inequality
- proof-of-fanos-inequality
- proof-of-law-of-large-numbers
- remark-14
- Reversibility of Markov Chains
- Self-Information
- Standard Normal Distribution
- State Space
- Sum of Independent Normal Random Variables
- Sum of Independent Poisson Random Variables
- theorem-16
- theorem-7
- Uniform Distribution
- Uniform distribution maximizes entropy
- Variance
Transitive (depth 3):
- Probability Density Function
- Hypergeometric Distribution
- note-25
- proof-of-chain-rule-for-mutual-information
- Geometric Distribution
- Probability of Error
- Conditioning Reduces Entropy
- Cross-Entropy
- doubly-stochastic-maps-increase-entropy-intuition
- Entropy Rate
- fanos-inequality-remark
- Gibbs' Inequality
- Chain rule for joint entropy
- KL Divergence
- lemma-93
- Noiseless channel transmitting discrete symbols
- note-8
- proof-of-concavity-of-entropy
- proof-of-doubly-stochastic-maps-increase-entropy
- proof-of-joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- proof-of-uniform-distribution-maximizes-entropy
- theorem-12
- Weak Asymptotic Equipartition Property
- Interpretation
- markovs-inequality-note
- proof-of-markovs-inequality
- proof-of-weak-asymptotic-equipartition-property
- proof-of-processing-cannot-increase-information
- proof-of-corollary-95
- proof-of-gibbs-inequality
- proof-of-lemma-93
- proof-of-log-sum-inequality
- proof-of-conditioning-reduces-entropy
- Conditional Relative Entropy
- Conditionally Independent
- proof-of-concavity-and-convexity-of-mutual-information
- Ergodic
- note-19
- proof-of-characterization-of-sufficiency
- proof-of-data-processing-inequality
- Stationary Distribution of an Ergodic Chain
- Strong Asymptotic Equipartition Property (Shannon--McMillan--Breiman)
- Sufficient Statistic
- Concavity and Convexity of Mutual Information
- proof-of-nonnegativity-of-mutual-information
- Bias
- Characterization of Sufficiency
- Conditional Probability Distribution
- Convexity of Relative Entropy
- Product Distribution
- proof-of-convexity-of-relative-entropy
- proof-of-nonnegativity-of-relative-entropy
- note-15
- remark-8
- note-3
- concavity-of-entropy-intuition
- Confidence Interval for a Mean, Known Variance
- Confidence Interval for a Mean, Unknown Variance
- Standard Deviation
- variance-interpretation
Transitive (depth 4):
- proof-of-joint-is-marginal-times-conditional
- proof-of-chain-rule-for-relative-entropy
- proof-of-nonnegativity-of-conditional-relative-entropy
- Nonnegativity of Conditional Mutual Information
- proof-of-conditional-independence-given-intermediate-step
- proof-of-nonnegativity-of-conditional-mutual-information
- Model Training
- proof-of-chain-rule-for-entropy
- proof-of-mutual-information-and-entropy
- convexity-of-relative-entropy-intuition
- remark-32
- Multiplication Rule for Distributions
- proof-of-corollary-77
- characterization-of-sufficiency-example
- Minimal Sufficient Statistic
We have a different - less general, but compatible - definition of connected from complex analysis:
This theorem helps connect the two definitions.
A subset of the real line is connected if and only if it has the following property: If and then
We will proceed both sides of the implication by proving the contrapositive, i.e., that if the interval property doesn't hold, then the set isn't connected, and conversely, that if the set isn't connected, the interval property doesn't hold.
Suppose and Then where
Since and they are nonempty, and since and they are separated. Therefore, is not connected.
Conversely, suppose, for the sake of contradiction, that is not connected. Then there are nonempty separated sets an such that Let and assume Define
By Let be a nonempty set of..., and because and are separated, Therefore
If it follows that and
If then hence there exists such that and (because means there is a neighborhood of that contains no points of .) Thus, and