![]() |
Metamath
Proof Explorer Theorem List (p. 307 of 429) | < Previous Next > |
Bad symbols? Try the
GIF version. |
||
Mirrors > Metamath Home Page > MPE Home Page > Theorem List Contents > Recent Proofs This page: Page List |
Color key: | ![]() (1-27903) |
![]() (27904-29428) |
![]() (29429-42879) |
Type | Label | Description |
---|---|---|
Statement | ||
Theorem | domprobsiga 30601 | The domain of a probability measure is a sigma-algebra. (Contributed by Thierry Arnoux, 23-Dec-2016.) |
⊢ (𝑃 ∈ Prob → dom 𝑃 ∈ ∪ ran sigAlgebra) | ||
Theorem | probtot 30602 | The probability of the universe set is 1. Second axiom of Kolmogorov. (Contributed by Thierry Arnoux, 8-Dec-2016.) |
⊢ (𝑃 ∈ Prob → (𝑃‘∪ dom 𝑃) = 1) | ||
Theorem | prob01 30603 | A probability is an element of [ 0 , 1 ]. First axiom of Kolmogorov. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃) → (𝑃‘𝐴) ∈ (0[,]1)) | ||
Theorem | probnul 30604 | The probability of the empty event set is 0. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ (𝑃 ∈ Prob → (𝑃‘∅) = 0) | ||
Theorem | unveldomd 30605 | The universe is an element of the domain of the probability, the universe (entire probability space) being ∪ dom 𝑃 in our construction. (Contributed by Thierry Arnoux, 22-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → ∪ dom 𝑃 ∈ dom 𝑃) | ||
Theorem | unveldom 30606 | The universe is an element of the domain of the probability, the universe (entire probability space) being ∪ dom 𝑃 in our construction. (Contributed by Thierry Arnoux, 22-Jan-2017.) |
⊢ (𝑃 ∈ Prob → ∪ dom 𝑃 ∈ dom 𝑃) | ||
Theorem | nuleldmp 30607 | The empty set is an element of the domain of the probability. (Contributed by Thierry Arnoux, 22-Jan-2017.) |
⊢ (𝑃 ∈ Prob → ∅ ∈ dom 𝑃) | ||
Theorem | probcun 30608* | The probability of the union of a countable disjoint set of events is the sum of their probabilities. (Third axiom of Kolmogorov) Here, the Σ construct cannot be used as it can handle infinite indexing set only if they are subsets of ℤ, which is not the case here. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ 𝒫 dom 𝑃 ∧ (𝐴 ≼ ω ∧ Disj 𝑥 ∈ 𝐴 𝑥)) → (𝑃‘∪ 𝐴) = Σ*𝑥 ∈ 𝐴(𝑃‘𝑥)) | ||
Theorem | probun 30609 | The probability of the union two incompatible events is the sum of their probabilities. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) → ((𝐴 ∩ 𝐵) = ∅ → (𝑃‘(𝐴 ∪ 𝐵)) = ((𝑃‘𝐴) + (𝑃‘𝐵)))) | ||
Theorem | probdif 30610 | The probability of the difference of two event sets. (Contributed by Thierry Arnoux, 12-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) → (𝑃‘(𝐴 ∖ 𝐵)) = ((𝑃‘𝐴) − (𝑃‘(𝐴 ∩ 𝐵)))) | ||
Theorem | probinc 30611 | A probability law is increasing with regard to event set inclusion. (Contributed by Thierry Arnoux, 10-Feb-2017.) |
⊢ (((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) ∧ 𝐴 ⊆ 𝐵) → (𝑃‘𝐴) ≤ (𝑃‘𝐵)) | ||
Theorem | probdsb 30612 | The probability of the complement of a set. That is, the probability that the event 𝐴 does not occur. (Contributed by Thierry Arnoux, 15-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃) → (𝑃‘(∪ dom 𝑃 ∖ 𝐴)) = (1 − (𝑃‘𝐴))) | ||
Theorem | probmeasd 30613 | A probability measure is a measure. (Contributed by Thierry Arnoux, 2-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → 𝑃 ∈ ∪ ran measures) | ||
Theorem | probvalrnd 30614 | The value of a probability is a real number. (Contributed by Thierry Arnoux, 2-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝐴 ∈ dom 𝑃) ⇒ ⊢ (𝜑 → (𝑃‘𝐴) ∈ ℝ) | ||
Theorem | probtotrnd 30615 | The probability of the universe set is finite. (Contributed by Thierry Arnoux, 2-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → (𝑃‘∪ dom 𝑃) ∈ ℝ) | ||
Theorem | totprobd 30616* | Law of total probability, deduction form. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝐴 ∈ dom 𝑃) & ⊢ (𝜑 → 𝐵 ∈ 𝒫 dom 𝑃) & ⊢ (𝜑 → ∪ 𝐵 = ∪ dom 𝑃) & ⊢ (𝜑 → 𝐵 ≼ ω) & ⊢ (𝜑 → Disj 𝑏 ∈ 𝐵 𝑏) ⇒ ⊢ (𝜑 → (𝑃‘𝐴) = Σ*𝑏 ∈ 𝐵(𝑃‘(𝑏 ∩ 𝐴))) | ||
Theorem | totprob 30617* | Law of total probability. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ (∪ 𝐵 = ∪ dom 𝑃 ∧ 𝐵 ∈ 𝒫 dom 𝑃 ∧ (𝐵 ≼ ω ∧ Disj 𝑏 ∈ 𝐵 𝑏))) → (𝑃‘𝐴) = Σ*𝑏 ∈ 𝐵(𝑃‘(𝑏 ∩ 𝐴))) | ||
Theorem | probfinmeasbOLD 30618* | Build a probability measure from a finite measure. (Contributed by Thierry Arnoux, 17-Dec-2016.) (New usage is discouraged.) (Proof modification is discouraged.) |
⊢ ((𝑀 ∈ (measures‘𝑆) ∧ (𝑀‘∪ 𝑆) ∈ ℝ+) → (𝑥 ∈ 𝑆 ↦ ((𝑀‘𝑥) /𝑒 (𝑀‘∪ 𝑆))) ∈ Prob) | ||
Theorem | probfinmeasb 30619 | Build a probability measure from a finite measure. (Contributed by Thierry Arnoux, 31-Jan-2017.) |
⊢ ((𝑀 ∈ (measures‘𝑆) ∧ (𝑀‘∪ 𝑆) ∈ ℝ+) → (𝑀∘𝑓/𝑐 /𝑒 (𝑀‘∪ 𝑆)) ∈ Prob) | ||
Theorem | probmeasb 30620* | Build a probability from a measure and a set with finite measure. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
⊢ ((𝑀 ∈ (measures‘𝑆) ∧ 𝐴 ∈ 𝑆 ∧ (𝑀‘𝐴) ∈ ℝ+) → (𝑥 ∈ 𝑆 ↦ ((𝑀‘(𝑥 ∩ 𝐴)) / (𝑀‘𝐴))) ∈ Prob) | ||
Syntax | ccprob 30621 | Extends class notation with the conditional probability builder. |
class cprob | ||
Definition | df-cndprob 30622* | Define the conditional probability. (Contributed by Thierry Arnoux, 14-Sep-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ cprob = (𝑝 ∈ Prob ↦ (𝑎 ∈ dom 𝑝, 𝑏 ∈ dom 𝑝 ↦ ((𝑝‘(𝑎 ∩ 𝑏)) / (𝑝‘𝑏)))) | ||
Theorem | cndprobval 30623 | The value of the conditional probability , i.e. the probability for the event 𝐴, given 𝐵, under the probability law 𝑃. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) → ((cprob‘𝑃)‘〈𝐴, 𝐵〉) = ((𝑃‘(𝐴 ∩ 𝐵)) / (𝑃‘𝐵))) | ||
Theorem | cndprobin 30624 | An identity linking conditional probability and intersection. (Contributed by Thierry Arnoux, 13-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ (((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) ∧ (𝑃‘𝐵) ≠ 0) → (((cprob‘𝑃)‘〈𝐴, 𝐵〉) · (𝑃‘𝐵)) = (𝑃‘(𝐴 ∩ 𝐵))) | ||
Theorem | cndprob01 30625 | The conditional probability has values in [0, 1]. (Contributed by Thierry Arnoux, 13-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ (((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) ∧ (𝑃‘𝐵) ≠ 0) → ((cprob‘𝑃)‘〈𝐴, 𝐵〉) ∈ (0[,]1)) | ||
Theorem | cndprobtot 30626 | The conditional probability given a certain event is one. (Contributed by Thierry Arnoux, 20-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ (𝑃‘𝐴) ≠ 0) → ((cprob‘𝑃)‘〈∪ dom 𝑃, 𝐴〉) = 1) | ||
Theorem | cndprobnul 30627 | The conditional probability given empty event is zero. (Contributed by Thierry Arnoux, 20-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ (𝑃‘𝐴) ≠ 0) → ((cprob‘𝑃)‘〈∅, 𝐴〉) = 0) | ||
Theorem | cndprobprob 30628* | The conditional probability defines a probability law. (Contributed by Thierry Arnoux, 23-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ ((𝑃 ∈ Prob ∧ 𝐵 ∈ dom 𝑃 ∧ (𝑃‘𝐵) ≠ 0) → (𝑎 ∈ dom 𝑃 ↦ ((cprob‘𝑃)‘〈𝑎, 𝐵〉)) ∈ Prob) | ||
Theorem | bayesth 30629 | Bayes Theorem. (Contributed by Thierry Arnoux, 20-Dec-2016.) (Revised by Thierry Arnoux, 21-Jan-2017.) |
⊢ (((𝑃 ∈ Prob ∧ 𝐴 ∈ dom 𝑃 ∧ 𝐵 ∈ dom 𝑃) ∧ (𝑃‘𝐴) ≠ 0 ∧ (𝑃‘𝐵) ≠ 0) → ((cprob‘𝑃)‘〈𝐴, 𝐵〉) = ((((cprob‘𝑃)‘〈𝐵, 𝐴〉) · (𝑃‘𝐴)) / (𝑃‘𝐵))) | ||
Syntax | crrv 30630 | Extend class notation with the class of real valued random variables. |
class rRndVar | ||
Definition | df-rrv 30631 | In its generic definition, a random variable is a measurable function from a probability space to a Borel set. Here, we specifically target real-valued random variables, i.e. measurable function from a probability space to the Borel sigma-algebra on the set of real numbers. (Contributed by Thierry Arnoux, 20-Sep-2016.) (Revised by Thierry Arnoux, 25-Jan-2017.) |
⊢ rRndVar = (𝑝 ∈ Prob ↦ (dom 𝑝MblFnM𝔅ℝ)) | ||
Theorem | rrvmbfm 30632 | A real-valued random variable is a measurable function from its sample space to the Borel sigma-algebra. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → (𝑋 ∈ (rRndVar‘𝑃) ↔ 𝑋 ∈ (dom 𝑃MblFnM𝔅ℝ))) | ||
Theorem | isrrvv 30633* | Elementhood to the set of real-valued random variables with respect to the probability 𝑃. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → (𝑋 ∈ (rRndVar‘𝑃) ↔ (𝑋:∪ dom 𝑃⟶ℝ ∧ ∀𝑦 ∈ 𝔅ℝ (◡𝑋 “ 𝑦) ∈ dom 𝑃))) | ||
Theorem | rrvvf 30634 | A real-valued random variable is a function. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → 𝑋:∪ dom 𝑃⟶ℝ) | ||
Theorem | rrvfn 30635 | A real-valued random variable is a function over the universe. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → 𝑋 Fn ∪ dom 𝑃) | ||
Theorem | rrvdm 30636 | The domain of a random variable is the universe. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → dom 𝑋 = ∪ dom 𝑃) | ||
Theorem | rrvrnss 30637 | The range of a random variable as a subset of ℝ. (Contributed by Thierry Arnoux, 6-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → ran 𝑋 ⊆ ℝ) | ||
Theorem | rrvf2 30638 | A real-valued random variable is a function. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → 𝑋:dom 𝑋⟶ℝ) | ||
Theorem | rrvdmss 30639 | The domain of a random variable. This is useful to shorten proofs. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → ∪ dom 𝑃 ⊆ dom 𝑋) | ||
Theorem | rrvfinvima 30640* | For a real-value random variable 𝑋, any open interval in ℝ is the image of a measurable set. (Contributed by Thierry Arnoux, 25-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → ∀𝑦 ∈ 𝔅ℝ (◡𝑋 “ 𝑦) ∈ dom 𝑃) | ||
Theorem | 0rrv 30641* | The constant function equal to zero is a random variable. (Contributed by Thierry Arnoux, 16-Jan-2017.) (Revised by Thierry Arnoux, 30-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → (𝑥 ∈ ∪ dom 𝑃 ↦ 0) ∈ (rRndVar‘𝑃)) | ||
Theorem | rrvadd 30642 | The sum of two random variables is a random variable. (Contributed by Thierry Arnoux, 4-Jun-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝑌 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → (𝑋 ∘𝑓 + 𝑌) ∈ (rRndVar‘𝑃)) | ||
Theorem | rrvmulc 30643 | A random variable multiplied by a constant is a random variable. (Contributed by Thierry Arnoux, 17-Jan-2017.) (Revised by Thierry Arnoux, 22-May-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐶 ∈ ℝ) ⇒ ⊢ (𝜑 → (𝑋∘𝑓/𝑐 · 𝐶) ∈ (rRndVar‘𝑃)) | ||
Theorem | rrvsum 30644 | An indexed sum of random variables is a random variable. (Contributed by Thierry Arnoux, 22-May-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋:ℕ⟶(rRndVar‘𝑃)) & ⊢ ((𝜑 ∧ 𝑁 ∈ ℕ) → 𝑆 = (seq1( ∘𝑓 + , 𝑋)‘𝑁)) ⇒ ⊢ ((𝜑 ∧ 𝑁 ∈ ℕ) → 𝑆 ∈ (rRndVar‘𝑃)) | ||
Syntax | corvc 30645 | Extend class notation to include the preimage set mapping operator. |
class ∘RV/𝑐𝑅 | ||
Definition | df-orvc 30646* |
Define the preimage set mapping operator. In probability theory, the
notation 𝑃(𝑋 = 𝐴) denotes the probability that a
random variable
𝑋 takes the value 𝐴. We
introduce here an operator which
enables to write this in Metamath as (𝑃‘(𝑋∘RV/𝑐 I 𝐴)), and
keep a similar notation. Because with this notation (𝑋∘RV/𝑐 I 𝐴)
is a set, we can also apply it to conditional probabilities, like in
(𝑃‘(𝑋∘RV/𝑐 I 𝐴) ∣ (𝑌∘RV/𝑐 I 𝐵))).
The oRVC operator transforms a relation 𝑅 into an operation taking a random variable 𝑋 and a constant 𝐶, and returning the preimage through 𝑋 of the equivalence class of 𝐶. The most commonly used relations are: - equality: {𝑋 = 𝐴} as (𝑋∘RV/𝑐 I 𝐴) cf. ideq 5307- elementhood: {𝑋 ∈ 𝐴} as (𝑋∘RV/𝑐 E 𝐴) cf. epel 5061- less-than: {𝑋 ≤ 𝐴} as (𝑋∘RV/𝑐 ≤ 𝐴) Even though it is primarily designed to be used within probability theory and with random variables, this operator is defined on generic functions, and could be used in other fields, e.g. for continuous functions. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ ∘RV/𝑐𝑅 = (𝑥 ∈ {𝑥 ∣ Fun 𝑥}, 𝑎 ∈ V ↦ (◡𝑥 “ {𝑦 ∣ 𝑦𝑅𝑎})) | ||
Theorem | orvcval 30647* | Value of the preimage mapping operator applied on a given random variable and constant. (Contributed by Thierry Arnoux, 19-Jan-2017.) |
⊢ (𝜑 → Fun 𝑋) & ⊢ (𝜑 → 𝑋 ∈ 𝑉) & ⊢ (𝜑 → 𝐴 ∈ 𝑊) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) = (◡𝑋 “ {𝑦 ∣ 𝑦𝑅𝐴})) | ||
Theorem | orvcval2 30648* | Another way to express the value of the preimage mapping operator. (Contributed by Thierry Arnoux, 19-Jan-2017.) |
⊢ (𝜑 → Fun 𝑋) & ⊢ (𝜑 → 𝑋 ∈ 𝑉) & ⊢ (𝜑 → 𝐴 ∈ 𝑊) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) = {𝑧 ∈ dom 𝑋 ∣ (𝑋‘𝑧)𝑅𝐴}) | ||
Theorem | elorvc 30649* | Elementhood of a preimage. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → Fun 𝑋) & ⊢ (𝜑 → 𝑋 ∈ 𝑉) & ⊢ (𝜑 → 𝐴 ∈ 𝑊) ⇒ ⊢ ((𝜑 ∧ 𝑧 ∈ dom 𝑋) → (𝑧 ∈ (𝑋∘RV/𝑐𝑅𝐴) ↔ (𝑋‘𝑧)𝑅𝐴)) | ||
Theorem | orvcval4 30650* | The value of the preimage mapping operator can be restricted to preimages in the base set of the topology. Cf. orvcval 30647. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑆 ∈ ∪ ran sigAlgebra) & ⊢ (𝜑 → 𝐽 ∈ Top) & ⊢ (𝜑 → 𝑋 ∈ (𝑆MblFnM(sigaGen‘𝐽))) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) = (◡𝑋 “ {𝑦 ∈ ∪ 𝐽 ∣ 𝑦𝑅𝐴})) | ||
Theorem | orvcoel 30651* | If the relation produces open sets, preimage maps by a measurable function are measurable sets. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑆 ∈ ∪ ran sigAlgebra) & ⊢ (𝜑 → 𝐽 ∈ Top) & ⊢ (𝜑 → 𝑋 ∈ (𝑆MblFnM(sigaGen‘𝐽))) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) & ⊢ (𝜑 → {𝑦 ∈ ∪ 𝐽 ∣ 𝑦𝑅𝐴} ∈ 𝐽) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) ∈ 𝑆) | ||
Theorem | orvccel 30652* | If the relation produces closed sets, preimage maps by a measurable function are measurable sets. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑆 ∈ ∪ ran sigAlgebra) & ⊢ (𝜑 → 𝐽 ∈ Top) & ⊢ (𝜑 → 𝑋 ∈ (𝑆MblFnM(sigaGen‘𝐽))) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) & ⊢ (𝜑 → {𝑦 ∈ ∪ 𝐽 ∣ 𝑦𝑅𝐴} ∈ (Clsd‘𝐽)) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) ∈ 𝑆) | ||
Theorem | elorrvc 30653* | Elementhood of a preimage for a real-valued random variable. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) ⇒ ⊢ ((𝜑 ∧ 𝑧 ∈ ∪ dom 𝑃) → (𝑧 ∈ (𝑋∘RV/𝑐𝑅𝐴) ↔ (𝑋‘𝑧)𝑅𝐴)) | ||
Theorem | orrvcval4 30654* | The value of the preimage mapping operator can be restricted to preimages of subsets of RR. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) = (◡𝑋 “ {𝑦 ∈ ℝ ∣ 𝑦𝑅𝐴})) | ||
Theorem | orrvcoel 30655* | If the relation produces open sets, preimage maps of a random variable are measurable sets. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) & ⊢ (𝜑 → {𝑦 ∈ ℝ ∣ 𝑦𝑅𝐴} ∈ (topGen‘ran (,))) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) ∈ dom 𝑃) | ||
Theorem | orrvccel 30656* | If the relation produces closed sets, preimage maps are measurable sets. (Contributed by Thierry Arnoux, 21-Jan-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝑉) & ⊢ (𝜑 → {𝑦 ∈ ℝ ∣ 𝑦𝑅𝐴} ∈ (Clsd‘(topGen‘ran (,)))) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐𝑅𝐴) ∈ dom 𝑃) | ||
Theorem | orvcgteel 30657 | Preimage maps produced by the "greater than or equal" relation are measurable sets. (Contributed by Thierry Arnoux, 5-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ ℝ) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐◡ ≤ 𝐴) ∈ dom 𝑃) | ||
Theorem | orvcelval 30658 | Preimage maps produced by the membership relation. (Contributed by Thierry Arnoux, 6-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝔅ℝ) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐 E 𝐴) = (◡𝑋 “ 𝐴)) | ||
Theorem | orvcelel 30659 | Preimage maps produced by the membership relation are measurable sets. (Contributed by Thierry Arnoux, 5-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ 𝔅ℝ) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐 E 𝐴) ∈ dom 𝑃) | ||
Theorem | dstrvval 30660* | The value of the distribution of a random variable. (Contributed by Thierry Arnoux, 9-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐷 = (𝑎 ∈ 𝔅ℝ ↦ (𝑃‘(𝑋∘RV/𝑐 E 𝑎)))) & ⊢ (𝜑 → 𝐴 ∈ 𝔅ℝ) ⇒ ⊢ (𝜑 → (𝐷‘𝐴) = (𝑃‘(◡𝑋 “ 𝐴))) | ||
Theorem | dstrvprob 30661* | The distribution of a random variable is a probability law. (TODO: could be shortened using dstrvval 30660) (Contributed by Thierry Arnoux, 10-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐷 = (𝑎 ∈ 𝔅ℝ ↦ (𝑃‘(𝑋∘RV/𝑐 E 𝑎)))) ⇒ ⊢ (𝜑 → 𝐷 ∈ Prob) | ||
Theorem | orvclteel 30662 | Preimage maps produced by the "lower than or equal" relation are measurable sets. (Contributed by Thierry Arnoux, 4-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ ℝ) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐 ≤ 𝐴) ∈ dom 𝑃) | ||
Theorem | dstfrvel 30663 | Elementhood of preimage maps produced by the "lower than or equal" relation. (Contributed by Thierry Arnoux, 13-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ ℝ) & ⊢ (𝜑 → 𝐵 ∈ ∪ dom 𝑃) & ⊢ (𝜑 → (𝑋‘𝐵) ≤ 𝐴) ⇒ ⊢ (𝜑 → 𝐵 ∈ (𝑋∘RV/𝑐 ≤ 𝐴)) | ||
Theorem | dstfrvunirn 30664* | The limit of all preimage maps by the "lower than or equal" relation is the universe. (Contributed by Thierry Arnoux, 12-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) ⇒ ⊢ (𝜑 → ∪ ran (𝑛 ∈ ℕ ↦ (𝑋∘RV/𝑐 ≤ 𝑛)) = ∪ dom 𝑃) | ||
Theorem | orvclteinc 30665 | Preimage maps produced by the "lower than or equal" relation are increasing. (Contributed by Thierry Arnoux, 11-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐴 ∈ ℝ) & ⊢ (𝜑 → 𝐵 ∈ ℝ) & ⊢ (𝜑 → 𝐴 ≤ 𝐵) ⇒ ⊢ (𝜑 → (𝑋∘RV/𝑐 ≤ 𝐴) ⊆ (𝑋∘RV/𝑐 ≤ 𝐵)) | ||
Theorem | dstfrvinc 30666* | A cumulative distribution function is non-decreasing. (Contributed by Thierry Arnoux, 11-Feb-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐹 = (𝑥 ∈ ℝ ↦ (𝑃‘(𝑋∘RV/𝑐 ≤ 𝑥)))) & ⊢ (𝜑 → 𝐴 ∈ ℝ) & ⊢ (𝜑 → 𝐵 ∈ ℝ) & ⊢ (𝜑 → 𝐴 ≤ 𝐵) ⇒ ⊢ (𝜑 → (𝐹‘𝐴) ≤ (𝐹‘𝐵)) | ||
Theorem | dstfrvclim1 30667* | The limit of the cumulative distribution function is one. (Contributed by Thierry Arnoux, 12-Feb-2017.) (Revised by Thierry Arnoux, 11-Jul-2017.) |
⊢ (𝜑 → 𝑃 ∈ Prob) & ⊢ (𝜑 → 𝑋 ∈ (rRndVar‘𝑃)) & ⊢ (𝜑 → 𝐹 = (𝑥 ∈ ℝ ↦ (𝑃‘(𝑋∘RV/𝑐 ≤ 𝑥)))) ⇒ ⊢ (𝜑 → 𝐹 ⇝ 1) | ||
Theorem | coinfliplem 30668 | Division in the extended real numbers can be used for the coin-flip example. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 /𝑒 2) | ||
Theorem | coinflipprob 30669 | The 𝑃 we defined for coin-flip is a probability law. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ 𝑃 ∈ Prob | ||
Theorem | coinflipspace 30670 | The space of our coin-flip probability. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ dom 𝑃 = 𝒫 {𝐻, 𝑇} | ||
Theorem | coinflipuniv 30671 | The universe of our coin-flip probability is {𝐻, 𝑇}. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ ∪ dom 𝑃 = {𝐻, 𝑇} | ||
Theorem | coinfliprv 30672 | The 𝑋 we defined for coin-flip is a random variable. (Contributed by Thierry Arnoux, 12-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ 𝑋 ∈ (rRndVar‘𝑃) | ||
Theorem | coinflippv 30673 | The probability of heads is one-half. (Contributed by Thierry Arnoux, 15-Jan-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ (𝑃‘{𝐻}) = (1 / 2) | ||
Theorem | coinflippvt 30674 | The probability of tails is one-half. (Contributed by Thierry Arnoux, 5-Feb-2017.) |
⊢ 𝐻 ∈ V & ⊢ 𝑇 ∈ V & ⊢ 𝐻 ≠ 𝑇 & ⊢ 𝑃 = ((# ↾ 𝒫 {𝐻, 𝑇})∘𝑓/𝑐 / 2) & ⊢ 𝑋 = {〈𝐻, 1〉, 〈𝑇, 0〉} ⇒ ⊢ (𝑃‘{𝑇}) = (1 / 2) | ||
Theorem | ballotlemoex 30675* | 𝑂 is a set. (Contributed by Thierry Arnoux, 7-Dec-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} ⇒ ⊢ 𝑂 ∈ V | ||
Theorem | ballotlem1 30676* | The size of the universe is a binomial coefficient. (Contributed by Thierry Arnoux, 23-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} ⇒ ⊢ (#‘𝑂) = ((𝑀 + 𝑁)C𝑀) | ||
Theorem | ballotlemelo 30677* | Elementhood in 𝑂. (Contributed by Thierry Arnoux, 17-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} ⇒ ⊢ (𝐶 ∈ 𝑂 ↔ (𝐶 ⊆ (1...(𝑀 + 𝑁)) ∧ (#‘𝐶) = 𝑀)) | ||
Theorem | ballotlem2 30678* | The probability that the first vote picked in a count is a B. (Contributed by Thierry Arnoux, 23-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) ⇒ ⊢ (𝑃‘{𝑐 ∈ 𝑂 ∣ ¬ 1 ∈ 𝑐}) = (𝑁 / (𝑀 + 𝑁)) | ||
Theorem | ballotlemfval 30679* | The value of F. (Contributed by Thierry Arnoux, 23-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ (𝜑 → 𝐶 ∈ 𝑂) & ⊢ (𝜑 → 𝐽 ∈ ℤ) ⇒ ⊢ (𝜑 → ((𝐹‘𝐶)‘𝐽) = ((#‘((1...𝐽) ∩ 𝐶)) − (#‘((1...𝐽) ∖ 𝐶)))) | ||
Theorem | ballotlemfelz 30680* | (𝐹‘𝐶) has values in ℤ. (Contributed by Thierry Arnoux, 23-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ (𝜑 → 𝐶 ∈ 𝑂) & ⊢ (𝜑 → 𝐽 ∈ ℤ) ⇒ ⊢ (𝜑 → ((𝐹‘𝐶)‘𝐽) ∈ ℤ) | ||
Theorem | ballotlemfp1 30681* | If the 𝐽 th ballot is for A, (𝐹‘𝐶) goes up 1. If the 𝐽 th ballot is for B, (𝐹‘𝐶) goes down 1. (Contributed by Thierry Arnoux, 24-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ (𝜑 → 𝐶 ∈ 𝑂) & ⊢ (𝜑 → 𝐽 ∈ ℕ) ⇒ ⊢ (𝜑 → ((¬ 𝐽 ∈ 𝐶 → ((𝐹‘𝐶)‘𝐽) = (((𝐹‘𝐶)‘(𝐽 − 1)) − 1)) ∧ (𝐽 ∈ 𝐶 → ((𝐹‘𝐶)‘𝐽) = (((𝐹‘𝐶)‘(𝐽 − 1)) + 1)))) | ||
Theorem | ballotlemfc0 30682* | 𝐹 takes value 0 between negative and positive values. (Contributed by Thierry Arnoux, 24-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ (𝜑 → 𝐶 ∈ 𝑂) & ⊢ (𝜑 → 𝐽 ∈ ℕ) & ⊢ (𝜑 → ∃𝑖 ∈ (1...𝐽)((𝐹‘𝐶)‘𝑖) ≤ 0) & ⊢ (𝜑 → 0 < ((𝐹‘𝐶)‘𝐽)) ⇒ ⊢ (𝜑 → ∃𝑘 ∈ (1...𝐽)((𝐹‘𝐶)‘𝑘) = 0) | ||
Theorem | ballotlemfcc 30683* | 𝐹 takes value 0 between positive and negative values. (Contributed by Thierry Arnoux, 2-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ (𝜑 → 𝐶 ∈ 𝑂) & ⊢ (𝜑 → 𝐽 ∈ ℕ) & ⊢ (𝜑 → ∃𝑖 ∈ (1...𝐽)0 ≤ ((𝐹‘𝐶)‘𝑖)) & ⊢ (𝜑 → ((𝐹‘𝐶)‘𝐽) < 0) ⇒ ⊢ (𝜑 → ∃𝑘 ∈ (1...𝐽)((𝐹‘𝐶)‘𝑘) = 0) | ||
Theorem | ballotlemfmpn 30684* | (𝐹‘𝐶) finishes counting at (𝑀 − 𝑁). (Contributed by Thierry Arnoux, 25-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) ⇒ ⊢ (𝐶 ∈ 𝑂 → ((𝐹‘𝐶)‘(𝑀 + 𝑁)) = (𝑀 − 𝑁)) | ||
Theorem | ballotlemfval0 30685* | (𝐹‘𝐶) always starts counting at 0 . (Contributed by Thierry Arnoux, 25-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) ⇒ ⊢ (𝐶 ∈ 𝑂 → ((𝐹‘𝐶)‘0) = 0) | ||
Theorem | ballotleme 30686* | Elements of 𝐸. (Contributed by Thierry Arnoux, 14-Dec-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} ⇒ ⊢ (𝐶 ∈ 𝐸 ↔ (𝐶 ∈ 𝑂 ∧ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝐶)‘𝑖))) | ||
Theorem | ballotlemodife 30687* | Elements of (𝑂 ∖ 𝐸). (Contributed by Thierry Arnoux, 7-Dec-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) ↔ (𝐶 ∈ 𝑂 ∧ ∃𝑖 ∈ (1...(𝑀 + 𝑁))((𝐹‘𝐶)‘𝑖) ≤ 0)) | ||
Theorem | ballotlem4 30688* | If the first pick is a vote for B, A is not ahead throughout the count. (Contributed by Thierry Arnoux, 25-Nov-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} ⇒ ⊢ (𝐶 ∈ 𝑂 → (¬ 1 ∈ 𝐶 → ¬ 𝐶 ∈ 𝐸)) | ||
Theorem | ballotlem5 30689* | If A is not ahead throughout, there is a 𝑘 where votes are tied. (Contributed by Thierry Arnoux, 1-Dec-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → ∃𝑘 ∈ (1...(𝑀 + 𝑁))((𝐹‘𝐶)‘𝑘) = 0) | ||
Theorem | ballotlemi 30690* | Value of 𝐼 for a given counting 𝐶. (Contributed by Thierry Arnoux, 1-Dec-2016.) (Revised by AV, 6-Oct-2020.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → (𝐼‘𝐶) = inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝐶)‘𝑘) = 0}, ℝ, < )) | ||
Theorem | ballotlemiex 30691* | Properties of (𝐼‘𝐶). (Contributed by Thierry Arnoux, 12-Dec-2016.) (Revised by AV, 6-Oct-2020.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → ((𝐼‘𝐶) ∈ (1...(𝑀 + 𝑁)) ∧ ((𝐹‘𝐶)‘(𝐼‘𝐶)) = 0)) | ||
Theorem | ballotlemi1 30692* | The first tie cannot be reached at the first pick. (Contributed by Thierry Arnoux, 12-Mar-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ ¬ 1 ∈ 𝐶) → (𝐼‘𝐶) ≠ 1) | ||
Theorem | ballotlemii 30693* | The first tie cannot be reached at the first pick. (Contributed by Thierry Arnoux, 4-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ 1 ∈ 𝐶) → (𝐼‘𝐶) ≠ 1) | ||
Theorem | ballotlemsup 30694* | The set of zeroes of 𝐹 satisfies the conditions to have a supremum. (Contributed by Thierry Arnoux, 1-Dec-2016.) (Revised by AV, 6-Oct-2020.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → ∃𝑧 ∈ ℝ (∀𝑤 ∈ {𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝐶)‘𝑘) = 0} ¬ 𝑤 < 𝑧 ∧ ∀𝑤 ∈ ℝ (𝑧 < 𝑤 → ∃𝑦 ∈ {𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝐶)‘𝑘) = 0}𝑦 < 𝑤))) | ||
Theorem | ballotlemimin 30695* | (𝐼‘𝐶) is the first tie. (Contributed by Thierry Arnoux, 1-Dec-2016.) (Revised by AV, 6-Oct-2020.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → ¬ ∃𝑘 ∈ (1...((𝐼‘𝐶) − 1))((𝐹‘𝐶)‘𝑘) = 0) | ||
Theorem | ballotlemic 30696* | If the first vote is for B, the vote on the first tie is for A. (Contributed by Thierry Arnoux, 1-Dec-2016.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ ¬ 1 ∈ 𝐶) → (𝐼‘𝐶) ∈ 𝐶) | ||
Theorem | ballotlem1c 30697* | If the first vote is for A, the vote on the first tie is for B. (Contributed by Thierry Arnoux, 4-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ 1 ∈ 𝐶) → ¬ (𝐼‘𝐶) ∈ 𝐶) | ||
Theorem | ballotlemsval 30698* | Value of 𝑆. (Contributed by Thierry Arnoux, 12-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) & ⊢ 𝑆 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ (𝑖 ∈ (1...(𝑀 + 𝑁)) ↦ if(𝑖 ≤ (𝐼‘𝑐), (((𝐼‘𝑐) + 1) − 𝑖), 𝑖))) ⇒ ⊢ (𝐶 ∈ (𝑂 ∖ 𝐸) → (𝑆‘𝐶) = (𝑖 ∈ (1...(𝑀 + 𝑁)) ↦ if(𝑖 ≤ (𝐼‘𝐶), (((𝐼‘𝐶) + 1) − 𝑖), 𝑖))) | ||
Theorem | ballotlemsv 30699* | Value of 𝑆 evaluated at 𝐽 for a given counting 𝐶. (Contributed by Thierry Arnoux, 12-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) & ⊢ 𝑆 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ (𝑖 ∈ (1...(𝑀 + 𝑁)) ↦ if(𝑖 ≤ (𝐼‘𝑐), (((𝐼‘𝑐) + 1) − 𝑖), 𝑖))) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ 𝐽 ∈ (1...(𝑀 + 𝑁))) → ((𝑆‘𝐶)‘𝐽) = if(𝐽 ≤ (𝐼‘𝐶), (((𝐼‘𝐶) + 1) − 𝐽), 𝐽)) | ||
Theorem | ballotlemsgt1 30700* | 𝑆 maps values less than (𝐼‘𝐶) to values greater than 1. (Contributed by Thierry Arnoux, 28-Apr-2017.) |
⊢ 𝑀 ∈ ℕ & ⊢ 𝑁 ∈ ℕ & ⊢ 𝑂 = {𝑐 ∈ 𝒫 (1...(𝑀 + 𝑁)) ∣ (#‘𝑐) = 𝑀} & ⊢ 𝑃 = (𝑥 ∈ 𝒫 𝑂 ↦ ((#‘𝑥) / (#‘𝑂))) & ⊢ 𝐹 = (𝑐 ∈ 𝑂 ↦ (𝑖 ∈ ℤ ↦ ((#‘((1...𝑖) ∩ 𝑐)) − (#‘((1...𝑖) ∖ 𝑐))))) & ⊢ 𝐸 = {𝑐 ∈ 𝑂 ∣ ∀𝑖 ∈ (1...(𝑀 + 𝑁))0 < ((𝐹‘𝑐)‘𝑖)} & ⊢ 𝑁 < 𝑀 & ⊢ 𝐼 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ inf({𝑘 ∈ (1...(𝑀 + 𝑁)) ∣ ((𝐹‘𝑐)‘𝑘) = 0}, ℝ, < )) & ⊢ 𝑆 = (𝑐 ∈ (𝑂 ∖ 𝐸) ↦ (𝑖 ∈ (1...(𝑀 + 𝑁)) ↦ if(𝑖 ≤ (𝐼‘𝑐), (((𝐼‘𝑐) + 1) − 𝑖), 𝑖))) ⇒ ⊢ ((𝐶 ∈ (𝑂 ∖ 𝐸) ∧ 𝐽 ∈ (1...(𝑀 + 𝑁)) ∧ 𝐽 < (𝐼‘𝐶)) → 1 < ((𝑆‘𝐶)‘𝐽)) |
< Previous Next > |
Copyright terms: Public domain | < Previous Next > |