# Monte Carlo methods

## Overview

• Attempts to solve two types of problems:
1. Generate samples from a given probability distribution
2. Estimate expectations of functions under this distribution, e.g.

• Class of algorithms for sampling from a probability distribution based on constructing a Markov chain that has the desired distribution as its equilibrium distribution.
• State of the chain after a number of steps is then used as a sample from the desired distribution
• Really good stuff about MCMC methods can be found in Chapter 29 and 30 in mackay2003information.

## Notation

• denotes the empirical expectation of
• denotes the partition function or normalization factor of distribution , i.e. we assume that defines a probability distribution.

## Importance Sampling (IS)

Consider the following

where we've let . Using MCMC methods, we can approximate this expectation

or equivalently,

This is importance sampling estimator of and is unbiased.

The importance sampling problem is then to find some biasing distribution such that the variance of the importance sampling estimator is lower than the variance for the general Monte Carlo estimate.

We can replace and with and , assuming

Then

where

Since

We have

with . That is, as , taking the unnormalized weigth we arrive at the same result as if we used the proper weights .

### Issues

#### Large number of dimensions

In a large number of dimensions, one will often have the problem that a small number of large weights will dominate the the sampling process. In the case where large values of lie in low-probability regions this becomes a huge issue.

## Rejection Sampling

• Have which is too complex to sample from
• We have a simpler density which we can evalute (up to a factor ) and raw samples from
• Assume that we know the value of a constant s.t.

• For :
1. Generate
2. Generate
3. Accept if , reject if
• Then

We're simply sampling from a distibution we can compute, and then computing the ratio of samples under which also fell under . Since we then know the "area" under we know that the area under is simply the ratio of points accepted.

This is basically a more general approach to the [BROKEN LINK: No match for fuzzy expression: *Box%20method%20Sampling], where we instead of having some distribution to sample from, we simply use a n-dimensional box (or rather, we let be a multi-dimensional ).

### Issues

#### Large number of dimensions

In large number of dimensions it's very likley that the requirement

forces to be so huge that acceptances become very rare.

Even finding in a high-dimensional space might be incredibly difficult.

## Markov Chain Monte Carlo (MCMC)

### Markov chains

A Markov chain is said to be reversible if there is a probability distribution over its states such that

for all times n and all states and . This condition is known as the detailed balance condition (some books call it the local balance equation).

### Methods

#### Metropolis-Hastings

The family of Metropolis-Hastings MCMC methods all involve designing a Markov process using two sub-steps:

1. Propose. Use some proposal distribution to propose the next state given the current state .
2. Acceptance-rejection. Use some acceptance distribution and accept / reject depending on this conditional distribution.

The transition probability is then

Inserting into the detailed balance equation for a MC we have

This is satisfied by (for example) the Metropolis choice:

The Metropolis-Hastings algorithm in its full glory is then:

1. Initialize
1. Pick initial state
2. Set
2. Iterate
1. Generate: randomly generate a candidate state according to
2. Compute: compute acceptance probability
3. Accept or Reject:
1. Generate a uniform random number
2. If , accept new state and set
3. If , reject new state and set
4. Increment: set

The empirical distribution of the states will approach .

When using MCMC methods for inference, it's important to remember that is usually the log-likelihood of the data.

This can also be seen by considering the posterior distribution of the parameters given the data, and considering the ratio between two different parameters, say and . Then

Letting the prior-distribution on be the proposal distribution in Metropolis-Hastings algorithm, we see that in this case we're simply taking steps towards the parameter which maximizes the log-likelihood under some prior.

Further, one could make the ratio by using a uniform distribution (if we know that for some .

The choice of is often referred to as a (transition) kernel when talking about different Metropolis-Hastings methods.

#### Gibbs

Gibbs sampling or a Gibbs sampler is a special case of a Metropolis-Hastings algorithm. The point of a Gibbs sampler is that given a multivariate distribution it is simpler to sample from a conditional distribution than to marginalize by integrating over a joint distribution.

Suppose we want to sample from a join distribution . Denote k-th sample by . We proceed as follows:

1. Initial value:
2. Sample . For :
1. Sample . If sampling from this conditional distribution is not tractable, then see Step 2 in the Metropolis-Hastings algorithm for how to sample.
3. Repeat above step until we have the number of samples we want.
##### Proof of convergence

Suppose we want to estimate the for a state .

Let

• denote the values which can take on, i.e. is all the possible states, keeping constant.
• denote some mass density over the indices (with non-zero mass everwhere)

Assuming , the Gibbs procedure gives us

where in

• we used

• we used

• we used the fact that

Therefore we have

which implies the detailed balance equation is satisfied, hence is the stationary distribution of the resulting Markov Chain.

Further, to ensure that this stationary distribution actually exists, observe that we can reach any state from the state in some number of steps, and that there are no "isolated" states, hence the chain constructed by the Gibbs transition creates an aperiodic and irreducible Markov Chain, which is guaranteed to converge to its stationary distribution .

In our definition of Gibbs sampling, we sample from the different dimensions sequentially, but here we have not assumed how sample the different dimensions, instead giving it some arbitrary distribution . Hence Gibbs sampling also works when not performing the sampling sequentially, but the sequential version is standard approach and usually what people refer to when talking about Gibbs sampling.

The notation instead of is often when talking about Markov Chains. In this case we're mainly interested in sampling from a distribution , therefore we instead let denote the stationary distribution of the chain.

In a Markov Random Field (MRF) we only need to marginalize over the immediate neighbours, so the conditional distribution can be written

##### Convergence bound

If is the transition matrix of the Gibbs chain, then the convergence rate of the periodic Gibbs sampler for the stationary distribution of an MRF is bounded by

where

• is the arbitrary initial distribution
• is the distance in variation

for distributions and on a finite state space

#### TODO Embedded MCMC (EMCMC)

##### Specific case: Hidden Markov-Models (HMMs)

The following assumes a state-space model where

• is the initial latent state
• is the transition probability
• is the emission/observation probability
• denotes the number of time-steps in the SSM
• Current sequence is
• Update to as follows:
1. For each time , generate a set of "pool states", denoted by

• Pool states are sampled independentlyo across different times
2. Choose uniformly at random and let

3. Sample remaining pool states using Markov chain that leaves the pool density invariant:
1. Let be the transition prob of this Markov chain, with the transition for this chain reversed, i.e.

s.t.

for all and .

2. For :
• Generate by sampling according to the reverse transition kernel
3. For :
• Generate by sampling according to the forward transition kernel
4. Generate new sequence using stochastic backwards pass:
1. For each time , we then compute the forward probabilities , with taking values in :
1. if :

2. else:

2. Sample new state sequence using a "stochastic backwards pass" (?):
1. Sample from (pool states at time ) with probabilities prop. to
2. For :
• Sample from (pool states at time ) with probabilities prob. to

Alternatively, we can replace step 4 above by instead computing the backward probabilities:

1. For , compute backward probabilities with :
1. if :

2. else:

2. Sample new state sequence using a stochastic forward pass:
1. Sample from with prob. prop. to
2. For :
• Sample from with prob. prop. to

#### TODO Unification of PMCMC and EMCMC andrieu2010particle

##### Notation
• is an latent Markov process satisfying

• is a sequence of observations which are conditionally independent given and satisfy

or, with a slighy abuse of notation,

• denotes the parameters of the model
##### Particle Independent Metropolis-Hastings (PIMH)
• Independent Metropolis-Hastings (IMH) refers to the fact that the proposal distribution is independent of the current state
• Proof idea:
1. Check that when
2. Deduce that acceptance ratio of an IMH update with target and proposal density takes the form

where denotes the estimate of the normalization constant for the proposition

• Proof
• If we assume (1), then we can quickly see that
• Construct a kernel which leaves the extended target distribution invariant, which is defined in such a way that we also leave the actual target density invariant

and the particle estimate

Note that

##### TODO Particle marginal Metropolis-Hastings (PMMH)
• Extended target distribution
• Proposal:

where

• is the law induced by bootstrap/resampling PF:

where

• is the prior for
• is the probability of the i-th particle taking on the value at time-step given that it's parent (indexed by ) has taken on the value
• is the probability of having chosen as the parent in for the i-th particle at time-step
• Resulting MH acceptance step is

where

is an unbiased estimate of

• The fact that it's correct can be shown by dividing the extended target by the probabililty of the auxillary variables:

since

and

##### PMCMC methods
• Notation
• denotes the number of particles
• represents the latent-state trajectory with "ancestor path" :

and thus is the indices of the reference path.

• Particles are denoted by bold-face:

• Ancestor particles

and

• Extended target distribution on :

• denotes the parameter domain
• is the domain of the hidden states in the state-space model for each of the particles
• represents all possible ancestor particles we can "possible generate" (you know what I mean…) since for any choice of , i.e. the first node (the root) of a path does not have an ancestor.
• In our case of considering SSMs, we have
• Conditional particle filter (CPF):

with

represents the normalized weight associated with the i-th particle at time

• Extended target distribution

To prove correctness we take the following approach:

1. Find kernel which leaves the extended target distribution invariant
2. Show that the PGAS kernel on the original space is a collapsed version of this extended kernel
• Showing that a Gibbs kernel is a collapsed Gibbs can be done by considering a kernel on the extended space where some of the variables on the extended space is considered as auxillary variables
3. For we're really just interested in the path chosen , and everything else is redundant information.
4. I.e.
• Note that is basically the product of the following distributions:
• From we pick the trajectory with indices
• We consider this jointly with the probability of generating all these other trajectories
• Inputs:
1. Parameters
2. Candidate path
• Note that ancestor indices alone does not define a path! These define a path through already sampled set of particles . Hence we need both the particles and the ancestor indices to define a path
• Proof of correctness

### Finding a starting point

#### Burn-In

• Throw away some iterations at the beginning of an MCMC run

### Issues

#### Dependent samples

The samples of a Markov Chain are in general dependent, since we're conditioning on the current state when sampling the proposal state.

## Partition function estimation

### Notation

• is a unnormalized probability distribution
• Probability distribution

with

### Overview

Log-likelihood is given by

These two terms are often referred to as:

1. postive phase
2. negative phase

The 2nd term in the summation can be written:

Interchanging integration and gradient, which we can do under under suitable conditions:

Hence,

Since the is constant, we observe that the second term in the above expression can be written

That is,

And, employing MCMC algorithms, we can estimate this expectation as:

i.e. are MCMC samples from the unnormalized .

Finally, giving use a more tractable gradient estimate of :

The exact same argument holds for discrete random variables too, simply exchanging the integrand for a summation.

#### Problems

• Using standard MCMC methods, we would have to perform a burnin before sampling those samples in the negative phase. This can be very expensive.

### Simulated Annealing (SA)

Simulated annealing is a method of moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions, used as an inexact method of handling isolated or spurrious modes in Markov chain samplers. Isolated modes is a problem for MCMC methods since we're making small changes between each step, hence moving from one isolated mode to another is "difficult".

It employes a sequence of distributions, with probability densities given by to , in which each differes only slightly from . The distribution is the one of interest. The distribution is designed so that the Markov chain used to sample from it allows movement between all regions of the state space.

A traditional scheme is to set

or the geometric average:

An annealing run is started from some initial state, from which we first simulate a Markov chain designed to converge to . Next we simulate some number of iterations of a Markov chain designed to converge to , starting from the final state of the simulation for . Continue in this fashion, using the final state of the simulation for as the initial state of the simulation for , until we finally simulate the chain designed to converge to .

Unfortunately, there is no reason to think that annealing will give the precisely correct result, in which each mode of is found with exactly the right probability.

This is of little consequence in an optimization context, where the final distribution is degenerate (at the maximum), but it is a serious flaw for the many applications in statistics and statistical physics that require a sample from a non-degenerate distribution. neal98_anneal_impor_sampl

### Annealed Importance Sampling (AIS)

#### Notation

• is a set of Gibbs distributions defined:

• , which we call the extended state space
• The use of square brackets, e.g. , denotes a distribution over
• We define

• (forward distribution) creates samples from given a sample from :

• (reverse distribution) creates samples from given a sample from :

#### Derivation

Assume that a set of Gibbs distributions to construct a pair of distributions and on with

• (forward distribution) creates samples from given a sample from
• (reverse distribution) creates samples from given a sample from

Then

where . Then, for a function , we have

thus the expectation of

Then, letting , i.e. constant function, we get

(Generalized) Annealed Importance Sampling (AIS) a method of estimating the partition function using the following relation:

where is the partition function of the known "proxy" distribution .

The "standard" AIS method is obtained by a specific choice of .

# Bibliography

• [mackay2003information] MacKay, Kay & Cambridge University Press, Information Theory, Inference and Learning Algorithms, Cambridge University Press (2003).
• [neal00_slice_sampl] Neal, Slice Sampling, CoRR, (2000). link.
• [del2004feynman] @incollectiondel2004feynman, title=Feynman-kac formulae, author=Del Moral, Pierre, keywords = particle methods,mcmc, booktitle=Feynman-Kac Formulae, pages=47-93, year=2004, publisher=Springer, file=/home/tor/Dropbox/books/machine_learning/delmoral2004.pdf:PDF
• [chopin2020introduction] Chopin & Papaspiliopoulos, An introduction to sequential Monte Carlo,.
• [finke2015extended] @phdthesisfinke2015extended, title=On extended state-space constructions for Monte Carlo methods, author=Finke, Axel
• [shestopaloff16_sampl_laten_states_high_dimen] Shestopaloff & Neal, Sampling Latent States for High-Dimensional Non-Linear State Space Models With the Embedded Hmm Method, CoRR, (2016). link.
• [andrieu2010particle] Andrieu, Doucet & Holenstein, Particle markov chain monte carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3), 269-342 (2010).
• [neal98_anneal_impor_sampl] Neal, Annealed Importance Sampling, CoRR, (1998). link.