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, 106 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
- Path Independent
- Random Variable
- remark-12
- Stoke's Theorem
- Taylor Expansion Theorem
- theorem-19
- Volume Integral
Transitive (depth 3):
- proof-of-cauchys-integral-theorem
- proof-of-integral-of-one-over-z-around-unit-circle
- theorem-7-intuition
- A/B Test
- Absolutely Continuous Random Variable
- Binomial Distribution
- chebyshev-fish
- Chebyshev's Inequality
- chebyshevs-inequality-note
- Cumulative Distribution Function
- Discrete Random Variable
- Entropy
- Expected Value
- Jensen's Inequality
- markov-inequality-fish
- Markov's Inequality
- Moment Generating Function
- Negative Binomial Distribution
- Normal Approximation to the Binomial
- Normal Distribution
- Poisson Approximation to the Binomial
- Poisson Distribution
- Probability Distribution
- proof-of-chebyshevs-inequality
- remark-14
- Self-Information
- Standard Normal Distribution
- State Space
- theorem-16
- Uniform Distribution
- Uniform distribution maximizes entropy
- Variance
Transitive (depth 4):
- Hypergeometric Distribution
- proof-of-law-of-large-numbers
- Identically Distributed
- Independent and Identically Distributed
- Mutual Independence
- Pairwise Independence
- Geometric Distribution
- Cross-Entropy
- doubly-stochastic-maps-increase-entropy-intuition
- Entropy Rate
- Gibbs' Inequality
- Chain rule for joint entropy
- joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- KL Divergence
- Noiseless channel transmitting discrete symbols
- note-25
- 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
- proof-of-markovs-inequality
- proof-of-weak-asymptotic-equipartition-property
- proof-of-gibbs-inequality
- proof-of-log-sum-inequality
- Moment Generating Function of a Sum
- Sum of Independent Normal Random Variables
- Sum of Independent Poisson Random Variables
- Conditional Probability Distribution
- Product Distribution
- 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
- Model Training
- Strong Asymptotic Equipartition Property (Shannon--McMillan--Breiman)
- proof-of-nonnegativity-of-conditional-mutual-information
- proof-of-nonnegativity-of-conditional-relative-entropy
- proof-of-nonnegativity-of-mutual-information
- proof-of-nonnegativity-of-relative-entropy
- Law of Large Numbers
- proof-of-chain-rule-for-entropy
- proof-of-mutual-information-and-entropy
- proof-of-theorem-54
- remark-32
- Multiplication Rule for Distributions
- Mutual Information
- theorem-7
- Central Limit Theorem
Transitive (depth 6):
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, 103 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
- Path Independent
- Random Variable
- remark-12
- Stoke's Theorem
- Taylor Expansion Theorem
- theorem-19
- Volume Integral
Transitive (depth 2):
- proof-of-cauchys-integral-theorem
- proof-of-integral-of-one-over-z-around-unit-circle
- theorem-7-intuition
- A/B Test
- Absolutely Continuous Random Variable
- Binomial Distribution
- chebyshev-fish
- Chebyshev's Inequality
- chebyshevs-inequality-note
- Cumulative Distribution Function
- Discrete Random Variable
- Entropy
- Expected Value
- Jensen's Inequality
- markov-inequality-fish
- Markov's Inequality
- Moment Generating Function
- Negative Binomial Distribution
- Normal Approximation to the Binomial
- Normal Distribution
- Poisson Approximation to the Binomial
- Poisson Distribution
- Probability Distribution
- proof-of-chebyshevs-inequality
- remark-14
- Self-Information
- Standard Normal Distribution
- State Space
- theorem-16
- Uniform Distribution
- Uniform distribution maximizes entropy
- Variance
Transitive (depth 3):
- Hypergeometric Distribution
- proof-of-law-of-large-numbers
- Identically Distributed
- Independent and Identically Distributed
- Mutual Independence
- Pairwise Independence
- Geometric Distribution
- Cross-Entropy
- doubly-stochastic-maps-increase-entropy-intuition
- Entropy Rate
- Gibbs' Inequality
- Chain rule for joint entropy
- joint-entropy-is-less-than-or-equal-to-entropy-of-parts
- KL Divergence
- Noiseless channel transmitting discrete symbols
- note-25
- 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
- proof-of-markovs-inequality
- proof-of-weak-asymptotic-equipartition-property
- proof-of-gibbs-inequality
- proof-of-log-sum-inequality
- Moment Generating Function of a Sum
- Sum of Independent Normal Random Variables
- Sum of Independent Poisson Random Variables
- Conditional Probability Distribution
- Product Distribution
- 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
- Model Training
- Strong Asymptotic Equipartition Property (Shannon--McMillan--Breiman)
- proof-of-nonnegativity-of-conditional-mutual-information
- proof-of-nonnegativity-of-conditional-relative-entropy
- proof-of-nonnegativity-of-mutual-information
- proof-of-nonnegativity-of-relative-entropy
- Law of Large Numbers
- proof-of-chain-rule-for-entropy
- proof-of-mutual-information-and-entropy
- proof-of-theorem-54
- remark-32
- Multiplication Rule for Distributions
- Mutual Information
- theorem-7
- Central Limit Theorem
Transitive (depth 5):
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