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| Type | Label | Description |
|---|---|---|
| Statement | ||
| Theorem | fib0 34401 | Value of the Fibonacci sequence at index 0. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘0) = 0 | ||
| Theorem | fib1 34402 | Value of the Fibonacci sequence at index 1. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘1) = 1 | ||
| Theorem | fibp1 34403 | Value of the Fibonacci sequence at higher indices. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (𝑁 ∈ ℕ → (Fibci‘(𝑁 + 1)) = ((Fibci‘(𝑁 − 1)) + (Fibci‘𝑁))) | ||
| Theorem | fib2 34404 | Value of the Fibonacci sequence at index 2. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘2) = 1 | ||
| Theorem | fib3 34405 | Value of the Fibonacci sequence at index 3. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘3) = 2 | ||
| Theorem | fib4 34406 | Value of the Fibonacci sequence at index 4. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘4) = 3 | ||
| Theorem | fib5 34407 | Value of the Fibonacci sequence at index 5. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘5) = 5 | ||
| Theorem | fib6 34408 | Value of the Fibonacci sequence at index 6. (Contributed by Thierry Arnoux, 25-Apr-2019.) |
| ⊢ (Fibci‘6) = 8 | ||
| Syntax | cprb 34409 | Extend class notation to include the class of probability measures. |
| class Prob | ||
| Definition | df-prob 34410 | Define the class of probability measures as the set of measures with total measure 1. (Contributed by Thierry Arnoux, 14-Sep-2016.) |
| ⊢ Prob = {𝑝 ∈ ∪ ran measures ∣ (𝑝‘∪ dom 𝑝) = 1} | ||
| Theorem | elprob 34411 | The property of being a probability measure. (Contributed by Thierry Arnoux, 8-Dec-2016.) |
| ⊢ (𝑃 ∈ Prob ↔ (𝑃 ∈ ∪ ran measures ∧ (𝑃‘∪ dom 𝑃) = 1)) | ||
| Theorem | domprobmeas 34412 | A probability measure is a measure on its domain. (Contributed by Thierry Arnoux, 23-Dec-2016.) |
| ⊢ (𝑃 ∈ Prob → 𝑃 ∈ (measures‘dom 𝑃)) | ||
| Theorem | domprobsiga 34413 | 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 34414 | 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 34415 | 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 34416 | The probability of the empty event set is 0. (Contributed by Thierry Arnoux, 25-Dec-2016.) |
| ⊢ (𝑃 ∈ Prob → (𝑃‘∅) = 0) | ||
| Theorem | unveldomd 34417 | 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 34418 | 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 34419 | 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 34420* | 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 34421 | 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 34422 | 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 34423 | 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 34424 | 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 34425 | A probability measure is a measure. (Contributed by Thierry Arnoux, 2-Feb-2017.) |
| ⊢ (𝜑 → 𝑃 ∈ Prob) ⇒ ⊢ (𝜑 → 𝑃 ∈ ∪ ran measures) | ||
| Theorem | probvalrnd 34426 | 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 34427 | 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 34428* | 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 34429* | 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 | probfinmeasb 34430 | 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 | probfinmeasbALTV 34431* | Alternate version of probfinmeasb 34430. (Contributed by Thierry Arnoux, 17-Dec-2016.) (New usage is discouraged.) |
| ⊢ ((𝑀 ∈ (measures‘𝑆) ∧ (𝑀‘∪ 𝑆) ∈ ℝ+) → (𝑥 ∈ 𝑆 ↦ ((𝑀‘𝑥) /𝑒 (𝑀‘∪ 𝑆))) ∈ Prob) | ||
| Theorem | probmeasb 34432* | 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 34433 | Extends class notation with the conditional probability builder. |
| class cprob | ||
| Definition | df-cndprob 34434* | 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 34435 | 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 34436 | 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 34437 | 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 34438 | 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 34439 | 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 34440* | 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 34441 | 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 34442 | Extend class notation with the class of real-valued random variables. |
| class rRndVar | ||
| Definition | df-rrv 34443 | 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 34444 | 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 34445* | 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 34446 | 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 34447 | 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 34448 | 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 34449 | 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 34450 | 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 34451 | 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 34452* | 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 34453* | 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 34454 | 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 34455 | 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 34456 | 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)) | ||
| Theorem | boolesineq 34457* | Boole's inequality (union bound). For any finite or countable collection of events, the probability of their union is at most the sum of their probabilities. (Suggested by DeepSeek R1.) (Contributed by Ender Ting, 30-Apr-2025.) |
| ⊢ ((𝑃 ∈ Prob ∧ 𝐴:ℕ⟶dom 𝑃) → (𝑃‘∪ 𝑛 ∈ ℕ (𝐴‘𝑛)) ≤ Σ*𝑛 ∈ ℕ(𝑃‘(𝐴‘𝑛))) | ||
| Syntax | corvc 34458 | Extend class notation to include the preimage set mapping operator. |
| class ∘RV/𝑐𝑅 | ||
| Definition | df-orvc 34459* |
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 5863- elementhood: {𝑋 ∈ 𝐴} as (𝑋∘RV/𝑐 E 𝐴) cf. epel 5587- 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 34460* | 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 34461* | 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 34462* | 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 34463* | The value of the preimage mapping operator can be restricted to preimages in the base set of the topology. Cf. orvcval 34460. (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 34464* | 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 34465* | 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 34466* | 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 34467* | The value of the preimage mapping operator can be restricted to preimages of subsets of ℝ. (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 34468* | 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 34469* | 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 34470 | Preimage maps produced by the "greater than or equal to" 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 34471 | 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 34472 | 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 34473* | 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 34474* | The distribution of a random variable is a probability law. (TODO: could be shortened using dstrvval 34473). (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 34475 | Preimage maps produced by the "less than or equal to" 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 34476 | Elementhood of preimage maps produced by the "less than or equal to" 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 34477* | The limit of all preimage maps by the "less than or equal to" 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 34478 | Preimage maps produced by the "less than or equal to" 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 34479* | A cumulative distribution function is nondecreasing. (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 34480* | 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 34481 | 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 34482 | 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 34483 | 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 34484 | 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 34485 | 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 34486 | 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 34487 | 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 34488* | 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 34489* | 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 34490* | 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 34491* | 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 34492* | 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 34493* | (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 34494* | 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 34495* | 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 34496* | 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 34497* | (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 34498* | (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 34499* | 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 34500* | 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)) | ||
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