Wow I really love Clock Tower 3

(as if most of you didn’t know that already)

But I always have wondered what kind of a game CT3 could have been had Capcom just cared more about it– it’s like they got the basics of a storyline down but then decided to go “fuck it” and left the pieces of it untied. The plot was clearly broken. There were some massive holes in it and it left a fuckton of things unexplained. What the finished game gave you was little bits of things here and there, (such as how entities are made, how some of the previous Rooders were killed, some limited background on the Subordinates), but nothing that felt fully developed.

My favorite thing to think about is the relationship between an entity and its subordinate. Why were those people specifically chosen? Do they even realize their entity when it first takes over them? Or does it take a ‘first death’ of the subordinate for the entity to really sink its teeth into the subordinate’s mind/fuctions, and that is what awakens their magical abilities?

It’s told that all five subordinates in the game had died at least once;

Sledgehammer died by hanging.

Corroder died by being dumped in a vat of acid.

Chopper died by an angry mob.

The Scissortwins died by being stoned to death.

All situations by which I feel like they could have avoided had they possessed magical power– nothing would have stopped them from teleporting away. That leads me to believe that most of their actions before their first death were mostly driven by their own agendas, with the entity only driving them a little over the edge instead of actually controlling their bodies and actions. It played on their emotions until it could finally take over the subordinate fully.

I also like to think about why subordinates are specifically chosen to be hosts for an entity. Surely hate comes into play, but since everyone feels an inkling of hate in their lives, the entity must be attracted to such high levels of hatred in a potential human host. Not only that, but the person they take over probably has to meet credentials that the separate entities decide for themselves. Sledgehammer’s entity must have preferred a blunt, crushing death by the hand of a very strong human– Robert Morris was ideal, in that sense, since he’d been a construction worker before his possession.

Chopper was also ideal for his entity, in the sense that he’d been a woodsman for a majority of his life– he was a strong, powerful host that could hold his own against an adversary, even without the use of magic. (Not to mention the rage Harold Powell had to feel from constant rejection due to his deformities.)

I feel like this is very similar for The Twins, except we’re not really given a lot of background on them– most of my basis for their backstory is just a whole lot of headcanons, but at least then it makes sense to me for why they were also picked to be subordinates. (My headcanon revolves around the possibility that maybe even children were hounded and coerced by entities to be the perfect host when they were older.)

I would go in depth about Corroder as well, but Corroder doesn’t seem like the type to actually give a shit so I figure he would have been a horrible, despicable man either way, :’D


Bayesian statistics

Hello friends

When I was preparing for my CT exams, I came across with a video explaining about Bayesian statistics by Allen Downey. After seeing that video I realized how easy the Bayesian statistics and how we can relate it with our practical life.

We can derive it by probability theorem.

Let us take probability of A and B to be true.

I.e. P (A and B)

Probability of A happening, times the probability of B happening. This is the case when the 2 events are independent.  I.e. P(A and B) = P(A).P(B)

But when the events are NOT independent, then we can say A is true and B is true given that A happens

P (A and B) = P(A).P(B/A) 

Let us take an example, suppose that you want to know the whether a person is married and have kids. So let the probability that the person is married be P(A).  Then the probability of having kids will be more if you know the marital status of that person, this implies if a person is married then he is having the more possibilities of having kids. I.e. P (B/A) (finding the probability of having kids given the information that the person is married.)

Or you can say that you know the person is having kids i.e. P(B) and you want to know the marital status of that person, so you can find it by P(A/B).

We can also write it as P(A and B)=P(B).P(A/B)

Now we have 2 equations   P (A and B) = P(A).P(B/A)   (1)

                                                  P(A and B)=P(B).P(A/B)      (2)

Now P(A/B)={ P(A).P(B/A)} / P(B)     {using (1) and (2)}

And this is known as the Bayes theorem named after Thomas Bayes (1701–1761), who first suggested using the theorem to update beliefs. His work was significantly edited and updated by Richard Price.

Now let us use this theorem in explaining the Bayesian statistics.

Let us consider H to be hypothesis and E is the evidence.  If you see evidence then you can update your belief in that hypothesis.

P(H) believe before you saw the evidence,  P(E/H) likelihood of seeing if your hypothesis of you is correct, P(E) likelihood of circumstances is an evidence.

P(H/E) (called posterior)={ P(H) (called the prior) * P(E/H) (called the likelihood)} / P(E) (called the normalizing constant).

Let us take an example. You are given 2 bowls containing red and yellow balls.

1st bowl contains 20 red and 40 yellow balls. And 2nd bowl contains 30 each balls. A person picked a random ball and it turns out to be a yellow ball. How predictable is that he picked it out of the 2nd bowl.

Here P(A)=½ (probability that the ball is picked from 1st bowl) = P(B) (probability that the ball is picked from 2nd bowl)

P(E/A)=4/6 (likelihood of evidence if it was taken from the A bowl)

P(E/B)=½ (likelihood of evidence if it was taken from the B bowl)

To find P(E) (evidence) we can add up all the probabilities from both the bowls.

P(E)= P(A)*P(E/A) + P(B)*P(E/B) = 7/12

So P(B/E)=P(B)*P(E/B)/P(E) = [(½)*(½)]/(7/12) =3/7

If the person picks from the 1st bowl then, P(A/E)= P(A)*P(E/A)/P(E) = [(½)*(4/6)]/(7/12) =4/7

Hence we conclude from the Bayes theorem

P(H/E)α [{P(H)*P(E/H)}/P(E)}]     

[ie believe about hypothesis seeing the evidence (P(H/E)) , how much you believe it before (P(H)), how likely was evidence if you were right (P(E/H)), likely circumstances under evidence you got (P(E)).]

Bt we can also ignore P(E) in solving the problem when we are given the distributions because it does not involve values from H and it is just the constant when we use the distribution, so when we integrate its probability turns out to be one.


We will discuss about the distributions later on.


clock tower 3 scissortwin headcanons


Anyway, Leanne and I were talking about our CT3 headcanons so here are all the ones I have for the Scissortwins, (mainly Ralph– I have less headcanons about Jemima because basically any of Mix’s headcanons for her are my headcanons);

  • Their parents were of the lower class. They loved the twins dearly, but were both killed by soldiers in times of war. Ralph and Jemima did not witness this– Ralph was promised by his father that they would return, and when they did not, he felt betrayed and distraught. (I used to think their father was a noble but eeehhh changed my mind.)
  • His distraught stems off the fact that Jemima was always very ill. He wasn’t sure if he’d be able to take care of her on his own, especially since he wasn’t very old at the time. He tried to help her anyway– feeling abandoned by his parents he couldn’t bear to lose his sister. 
  • Jemima was doomed to die. Her health would get worse and worse, and it drove Ralph further into despair– he tried to pretend like she’d somehow get better, and he never gave up on her. Unfortunately in his desperation to keep her alive he would get beaten for stealing, or go without food himself so that she could eat. It wore down on him physically and mentally to the point where he would break down when he thought Jemima wasn’t looking. (She noticed this sometimes anyways, as she’d wake up to him crying or punching the walls.)

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