Contacts are defined by a cut-off distance between CA or CB atoms. Some of this functionality is also integrated in peptide. The program can also produce a distance map. Calculation of B-factors for a simulation trajectory is reliable only if the trajectory is very short less than steps per residue. Basic statistical pipe tool to calculate means, standard deviations, statistical inefficiencies and extreme values for a sequence of numbers separated by whitespace.
Merges and sorts output files that are produced during a Sun MPI parallel-tempered run using "mprun -B". The files to be merged should be listed on the command line. The script and the merg executable are useful only on Sun clusters. An example script to set up Contrastive Divergence learning see below from a dataset of PDB structures.
The script uses 2 parameters: the number of available cluster nodes and a file with the list of PDB id's. Each node receives a PBS job diverge. PDB viewer produces a stereo pair in a printable PostScript format. It uses a PDB file name as a command line parameter. PDB files are parsed by the oops executable that is called from the script.
By default, all the programs read data from the standard input stdin and write to the standard output stdout , so that one can use them in a pipeline. If a file name is given on the command line, the input is redirected from this file. The option "-o filename" allows one to redirect the output to a file.
This is an example of a pipeline that involves most programs in the suite. We implemented an alternative way of setting initial conformation for peptide. One can enter a sequence of residues directly on the command line, e. This will run simulations on the residue polypeptide, starting from the first 8 residues in alpha-helical state and the last 8 residues in beta-strand conformation. The default force-field used by peptide includes hard-core van der Waals repulsions and square-well interpeptide hydrogen bonding.
One can also specify Go-type interactions between side-chains by providing a regularized contact map. For example,. The above example starts simulations from alpha-helical conformation and turns it into a beta-hairpin in less than 15 seconds on a 3. The peptide 's option "-p" is perhaps the most powerful option, which allows one to control many parameters of the program, including force-field constants. This option is intended to be customizable with flexible format.
To learn more about this option in the current version, we refer the reader to the source code. Below we describe some details of the Metropolis sampler implementation. To understand this, consider a common method of approximating the area of a unit ciricle by sampling points from the square with side length 2, and accepting those samples that are within the circle.
Note that this is secretly approximating the uniform distribution on the unit circle using rejection sampling. This will converge, if a little slowly. In n -dimensions, this cube has volume 2 n , while the volume of a hypersphere converges to zero. This is an unintuitive fact, that all of the volume in a high dimensional cube is in the corners, that should also serve as a warning that our geometric hunches may not be accurate in high dimensions. I did not actually believe the math here was as bad as all that, and tried this with numpy.
In a recurring theme today, math works, and in ten million samples from a twenty dimensional cube, none of them were inside the unit hypersphere. In searching for a general sampling method, we turn to Markov Chain Monte Carlo.
A Markov chain is a discrete process where each step has knowledge of the previous step. This is my dog Pete, exhibiting how a Markov chain might explore a region of high scent density click through for gif. He wanders off briefly in the middle, but then goes back to the good stuff. This is roughly how Metropolis-Hastings works.
If he was good at following scents, I might have another video of him demonstrating Hamiltonian Monte Carlo, but he does not want to go too far from the good stuff. This is a cartoon of how Metropolis Hastings sampling works. Suppose we have a two dimensional probability distribution, where the pdf is like a donut: there is a ring of high likelihood at radius 1, and it decays like a Gaussian around that.
Metropolis-Hastings is a two step process of making proposals, and then deciding whether or not to accept that proposal. When we reject a proposal, we add the current point back to our samples again. This acceptance step does the mathematical bookkeeping to make sure that, in the limit, we are sampling from the correct distribution.
Notice this is a Markov chain, because our proposal will depend on our current position, but not on our whole history. For more rigorous approach, we can look at the actual algorithm.
You can see the two steps here: first, propose a point, then accept or reject that point. We will again start with a proposal distribution, but this time it is conditioned on the current point, and suggests a place to move to. A common choice is a normal distribution, centered at your current point with some fixed step size.
We then accept or reject that choice, based on the probability at the proposed point. Either way we get a new sample every iteration, though it is problematic if you have too many rejections or too many acceptances. There are some technical concerns when choosing your proposal, and the acceptance here is only for symmetric proposal distributions, though the more general rule is also very simple.
But this is it, and this is a big selling point of Metropolis-Hastings: it is easy to implement, inspect, and reason about. Another thing to reflect on with this algorithm is that our samples will be correlated, in proportion to our step size. SCALE provides a comprehensive, verified and validated, user-friendly tool set for criticality safety, reactor and lattice physics, radiation shielding, spent fuel and radioactive source term characterization, and sensitivity and uncertainty analysis.
Since , regulators, licensees, and research institutions around the world have used SCALE for safety analysis and design.
SCALE provides an integrated framework with dozens of computational modules including three deterministic and three Monte Carlo radiation transport solvers that are selected based on the desired solution strategy. SCALE includes current nuclear data libraries and problem-dependent processing tools for continuous-energy CE and multigroup MG neutronics and coupled neutron-gamma calculations, as well as activation, depletion, and decay calculations.
SCALE includes unique capabilities for automated variance reduction for shielding calculations, as well as sensitivity and uncertainty analysis. Two variants of KENO provide identical solution capabilities with different geometry packages.
KENO V. Both versions of KENO perform eigenvalue calculations for neutron transport primarily to calculate multiplication factors keff and flux distributions of fissile systems in both CE and MG modes. Criticality safety analysts may also be interested in the sensitivity and uncertainty analysis techniques that can be applied for code and data validation as described elsewhere in this document. The intention of the sequence is to calculate fluxes and dose rates with low uncertainties in reasonable times, even for deep penetration problems.
MAVRIC generates problem-dependent cross section data, and then it automatically performs a coarse mesh 3D discrete ordinates transport calculation using Denovo to determine the adjoint flux as a function of position and energy, and apply the information to optimize the shielding calculation in Monaco. We thus perform a half-step update of the velocity at time , which is then used to compute and. In practice, using finite stepsizes will not preserve exactly and will introduce bias in the simulation.
Also, rounding errors due to the use of floating point numbers means that the above transformation will not be perfectly reversible. The new state is accepted with probability , defined as:.
In Theano, update dictionaries and shared variables provide a natural way to implement a sampling algorithm. The current state of the sampler can be represented as a Theano shared variable, with HMC updates being implemented by the updates list of a Theano function. To perform leapfrog steps, we first need to define a function over which can iterate over.
Instead of implementing Eq. In loop form, this gives:. The inner-loop defined above is implemented by the following function, with , and replacing and respectively. The function performs the full algorithm of Eqs. We start with the initial half-step update of and full-step of , and then scan over the method times.
A final half-step is performed to compute , and the final proposed state is returned. The function implements the remaining steps steps 1 and 3 of an HMC move proposal while wrapping the function.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. In Risk Theory Beard, Pentikanen and Pesonen mention a method of assessing number of samples needed for Monte Carlo simulation as. This is similar to simulation standard error estimation based on observed variance mentioned by Aksakal.
The idea here is that we want to estimate the probability of an event by using a sample proportion across many Monte Carlo trials, and we want to know how accurate of an estimate that proportion is of the true probability. This may end up causing us to run more simulations than we need to, but this won't matter as long as the iterations themselves are computationally cheap. This approximation is called Wald confidence interval.
Also refer to the archived Monte Python 2 forum for additional previously answered questions, but please post all new issues on the Monte Python 3 forum. Monte Python is developed and maintained by volunteer workers and we are always happy for new people to contribute. Do not hesitate to contact us if you believe you have something to add, this can be e. Additionally, everyone is encouraged to assist in resolving issues on the forum, so do not hesitate to reply if you think you can help.
In particular, if you would like to have your likelihood added to the public github repository, please make sure it is well documented and add all relevant information to the. This will create a directory named montepython into your current directory. You can add the following line to your. You will need to adapt only two files to your local configuration.
This should be changed to wherever is your preferred python distribution installed. For standard distribution, this should already be working.
Now, you should be able to execute directly the file, i. The second file to modify is located in the root directory of Monte Python : default. This file will be read and stored whenever you execute the program, and will search for your cosmological code path, your data path, and your wmap wrapper path. You can alternatively create a second one, my.
1. Introduction Parallel computation with Hamiltonian Monte Carlo. Hamiltonian Monte Carlo is a Markov chain Monte Carlo method for approximating integrals with respect to a target probability distribution |$\pi$| on |$\mathbb{R}^{d}$|.Originally proposed by Duane et al. () in the physics literature, it was later introduced into statistics by Neal () and is now widely .Education WordPress Theme By Logical Themes