We are going to apply the **theory**
of based on observations hypotheses reevaluation, to the *no-life* on Mars
hypothesis. We have to start from __the higher margin__ of *a priory *probability
of the *no-life* hypothesis, relative to the *life* hypothesis, for
example **R=100000**.

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Then the theory allows us to calculate the **R** after
series of observations **ABCDE…,**

** **

** R|ABCDE… = R *
K(A) * K(B) * K(C) * K(D) * K(E) * …**

where**
K(**X**)=P(**X**|H)/P(**X**|~H)**

If we have enough observations, the initial bias of the hypothesis relative probability estimate does not really matter /if it is not zero/, since the corrections will eventually overcome it.

Conditional probabilities of observed facts **A, B, …** must be estimated from the available data.
More precisely, we need centered or lower bound estimate under the *life*
condition, and upper bound estimate under the *no life* condition.

The objects of interest are those that appear, compared to
others, the most likely to be of the biological origin. Such objects with
practical certainty are of the biological origin, on the condition that *life
*hypothesis is correct.

A target object is being presented as a base and a number of features. The base’s probability is being estimated from its relative observed frequency in the imagery.

The features’ probabilities are being estimated as conditioned on the base’s presence - using applicable physical considerations, and/or relative observed feature frequency in the imagery.

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Under the *no*-*life *hypothesis features **b, c,
d …** are independent:

**P(b, c, d
...)=p(b)*p(c)*p(d)…**

Under the *life *hypothesis there is a relatively high
conditional probability

** P(b, c,
d...)=p(a)*P(c, d...|b)**

We consider the **K(~H|A) , **with** H **being** **the
*life *hypothesis and **b** the base feature:

**K(~H|A)=(
p(c)*p(d)*…) / P(c, d...|b)**

To measure with sufficient precision the probabilities involved, we can count the pertinent objects, observed in the imagery.

** **

**E****stimating particular
observations.**

*Basic estimates:*

*Total relevant images* 10,000

*Image area size* 10x10 m

*Relevant objects in the area* 10

*Total covered area* 1,000,000 m2

*Total objects* 100,000

*Average object area* 10 m2

*Unique feature probability* 1/10,000

**Martian**

**K(~H|A)** is estimated as probability of having a
white, egg shape rock, of size larger
than** base_size/10**, on top of the black base rock.

Egg shape white rocks probability < 1/100.

Egg shape white rocks count < 1000.

Getting on top of base rock < 1000 * 1/1,000,000 * 1/10=1/10,000

Suit feature probability ~1/10,000 < 1/1000

**K(~H|A)=1/10,000,000**

** **

** **

**Hooked
creature**

** **

Hook feature probability ~1/10,000

**K(~H|A)=1/10,000**

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** The axe**

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**K(~H|A)<1/10,000**

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**The flowers**

**K(~H|A)<1/10,000**

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**Berries on
stalk**

Bent stalk feature probability <1/1000

Berry on a bent stalk << 1/1,000,000

Double 10^-18

** **

**K(~H|A)<10^-18**

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** A girl**

** K(~H|A)<1/10000**

** **

** **

** A girl head portrait**

** K(~H|A)<10^-8**

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** Coin**

** K(~H|A)<1/10000**

More than two unique features, prominently the blade

** K(~H|A)<< 10^-8**

** K(~H|A)< 10^-4**

** K(~H|A)< 10^-4**

** K(~H|A)< 10^-4 **

* * *

The current total no-life hypothesis probability reduction coefficient:

**10^73**

Using 3-sigma rule, estimate of lower limit of the *no-life *hypothesis probability reduction
coefficient is:

**10^24**

*This section will expand, as the additional estimates are being done.

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