Bayesian Experimental Design for Stochastic Kinetic ModelsColin Gillespie
In recent years, the use of the Bayesian paradigm for estimating the optimal experimental design has increased. However, standard techniques are
computationally intensive for even relatively small stochastic kinetic models. One solution to this problem is to couple cloud computing with a model emulator.
By running simulations simultaneously in the cloud, the large design space can be explored. A Gaussian process is then fitted to this output, enabling the
optimal design parameters to be estimated.
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Hathor is a cryptocurrency platform for the creation of independent tokens. Each issued token has the full security of the Hathor blockchain. These are some context notes regarding the development of the project. They could be of use to computer scientists, applied mathematicians, and maybe one or two crypto investors.
Detail Description about Probability Distribution for Dummies. The contents are about random variables, its types(Discrete and Continuous) , it's distribution (Discrete probability distribution and probability density function), Expected value, Binomial, Poisson and Normal Distribution usage and solved example for each topic.
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2. Exp. :- VErify somE of law of probability
by throwing of similar coin likE objEct.
Apparatus:- A few identical coin, a plastic smooth mug, a wooden tray.
Record:- With one coin
Number of throw N = 100.
Number of coin n = 1.
(Make the record as shown)
Result:-
Number of time heads up = h = 54
Probability of getting head up =1/2
No. of times head should be up theoretically= 50
Percentage variation = (100-h)/100*50 = 23
No. 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 … 98 99 100
Heads
up
H H H H H H H H … … T T
Tails
up
T T T T T T … … H
3. With TWO coins:-
No. of throws = N =100.
No. of coins = n= 2.
(Make the record as shown)
Result :- Exp. Count of h,h = A = 13
Exp. Count of t,t = B = 32
Exp count of h,t = C = 55
Theoretical prob. Of h,h or h,t = 0.25
No. of trials are throws =100
.’. Theoretical prob. = 100 * 0.25 =25
Percentage deviation for h,h = (25-A)/25*100 = 48
No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 … 98 99 100
HH hh … … … hh
TT tt tt tt tt … … … tt
HT ht ht ht ht ht ht ht ht ht … … …
4. No. of tails = 100
.’. Theoretical prob. = 100 * 0.5 =50
Percentage deviation = (50-C)/25 *100 = 20
With any no. of coins:-
No. of throws N =100
No. of coin n =10
(Make the record as shown)
6. Result:-
Examine the deviation in each case. It is observed that as the no. of
trials is increased, the deviation goes on falling.
From the observation made above, it is possible to make following
conclusion:
1.With one coin the no. of times we get head up is nearly the same
as the expected value given by the product of the total throws and
probability of heads up i.e. N*P(h). This prove the principle of a
priori prob.
2. With two coin system
No. of times of hh + no. of times of tt + no. of times of ht is always
equal to total no. of trials.
No. of times the head of both are up i.e. n(h,h) is nearly equal to the
product of total no, of throws and the prob. of the heads being up or
N*P(hh).
3.With large no. of coins the actual frequency obtained exp. For diff.
7. Result:-
Examine the deviation in each case. It is observed that as the no. of
trials is increased, the deviation goes on falling.
From the observation made above, it is possible to make following
conclusion:
1.With one coin the no. of times we get head up is nearly the same
as the expected value given by the product of the total throws and
probability of heads up i.e. N*P(h). This prove the principle of a
priori prob.
2. With two coin system
No. of times of hh + no. of times of tt + no. of times of ht is always
equal to total no. of trials.
No. of times the head of both are up i.e. n(h,h) is nearly equal to the
product of total no, of throws and the prob. of the heads being up or
N*P(hh).
3.With large no. of coins the actual frequency obtained exp. For diff.