Chapter 17
Monte Carlo Methods
Randomized algorithms fall into two rough categories: Las Vegas algorithms and
Monte Carlo algorithms. Las Vegas algorithms always return precisely the correct
answer (or report that they failed). These algorithms consume a random amount
of resources, usually memory or time. In contrast, Monte Carlo algorithms return
answers with a random amount of error. The amount of error can typically be
reduced by expending more resources (usually running time and memory). For any
fixed computational budget, a Monte Carlo algorithm can provide an approximate
answer.
Many problems in machine learning are so difficult that we can never expect to
obtain precise answers to them. This excludes precise deterministic algorithms and
Las Vegas algorithms. Instead, we must use deterministic approximate algorithms
or Monte Carlo approximations. Both approaches are ubiquitous in machine
learning. In this chapter, we focus on Monte Carlo methods.
17.1 Sampling and Monte Carlo Methods
Many important technologies used to accomplish machine learning goals are based
on drawing samples from some probability distribution and using these samples to
form a Monte Carlo estimate of some desired quantity.
17.1.1 Why Sampling?
We may wish to draw samples from a probability distribution for many reasons.
Sampling provides a flexible way to approximate many sums and integrals at
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CHAPTER 17. MONTE CARLO METHODS
reduced cost. Sometimes we use this to provide a significant speedup to a costly
but tractable sum, as in the case when we subsample the full training cost with
minibatches. In other cases, our learning algorithm requires us to approximate an
intractable sum or integral, such as the gradient of the log partition function of an
undirected model. In many other cases, sampling is actually our goal, in the sense
that we want to train a model that can sample from the training distribution.
17.1.2 Basics of Monte Carlo Sampling
When a sum or an integral cannot be computed exactly (for example, the sum
has an exponential number of terms, and no exact simplification is known), it is
often possible to approximate it using Monte Carlo sampling. The idea is to view
the sum or integral as if it were an expectation under some distribution and to
approximate the expectation by a corresponding average. Let
s =
x
p(x)f(x) = E
p
[f(x)] (17.1)
or
s =
p(x)f(x)dx = E
p
[f(x)] (17.2)
be the sum or integral to estimate, rewritten as an expectation, with the constraint
that
p
is a probability distribution (for the sum) or a probability density (for the
integral) over random variable x.
We can approximate
s
by drawing
n
samples
x
(1)
, . . . , x
(n)
from
p
and then
forming the empirical average
ˆs
n
=
1
n
n
i=1
f(x
(i)
). (17.3)
This approximation is justified by a few different properties. The first trivial
observation is that the estimator ˆs is unbiased, since
E[ˆs
n
] =
1
n
n
i=1
E[f(x
(i)
)] =
1
n
n
i=1
s = s. (17.4)
But in addition, the
law of large numbers
states that if the samples
x
(i)
are
i.i.d., then the average converges almost surely to the expected value:
lim
n→∞
ˆs
n
= s, (17.5)
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CHAPTER 17. MONTE CARLO METHODS
provided that the variance of the individual terms,
Var
[
f
(
x
(i)
)], is bounded. To see
this more clearly, consider the variance of
ˆs
n
as
n
increases. The variance
Var
[
ˆs
n
]
decreases and converges to 0, so long as Var[f(x
(i)
)] < :
Var[ˆs
n
] =
1
n
2
n
i=1
Var[f(x)] (17.6)
=
Var[f(x)]
n
. (17.7)
This convenient result also tells us how to estimate the uncertainty in a Monte
Carlo average or equivalently the amount of expected error of the Monte Carlo
approximation. We compute both the empirical average of the
f
(
x
(i)
) and their
empirical variance,
1
and then divide the estimated variance by the number of
samples
n
to obtain an estimator of
Var
[
ˆs
n
]. The
central limit theorem
tells
us that the distribution of the average,
ˆs
n
, converges to a normal distribution
with mean
s
and variance
Var[f(x)]
n
. This allows us to estimate confidence intervals
around the estimate ˆs
n
, using the cumulative distribution of the normal density.
All this relies on our ability to easily sample from the base distribution
p
(
x
),
but doing so is not always possible. When it is not feasible to sample from
p
, an
alternative is to use importance sampling, presented in section 17.2. A more general
approach is to form a sequence of estimators that converge toward the distribution
of interest. That is the approach of Monte Carlo Markov chains (section 17.3).
17.2 Importance Sampling
An important step in the decomposition of the integrand (or summand) used by the
Monte Carlo method in equation 17.2 is deciding which part of the integrand should
play the role of probability
p
(
x
) and which part of the integrand should play the
role of the quantity
f
(
x
) whose expected value (under that probability distribution)
is to be estimated. There is no unique decomposition because
p
(
x
)
f
(
x
) can always
be rewritten as
p(x)f(x) = q(x)
p(x)f(x)
q(x)
, (17.8)
where we now sample from
q
and average
pf
q
. In many cases, we wish to compute
an expectation for a given
p
and an
f
, and the fact that the problem is specified
from the start as an expectation suggests that this
p
and
f
would be a natural
1
The unbiased estimator of the variance is often preferred, in which the sum of squared
differences is divided by n 1 instead of n.
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CHAPTER 17. MONTE CARLO METHODS
choice of decomposition. However, the original specification of the problem may
not be the the optimal choice in terms of the number of samples required to obtain
a given level of accuracy. Fortunately, the form of the optimal choice
q
can be
derived easily. The optimal
q
corresponds to what is called optimal importance
sampling.
Because of the identity shown in equation 17.8, any Monte Carlo estimator
ˆs
p
=
1
n
n
i=1,x
(i)
p
f(x
(i)
) (17.9)
can be transformed into an importance sampling estimator
ˆs
q
=
1
n
n
i=1,x
(i)
q
p(x
(i)
)f(x
(i)
)
q(x
(i)
)
. (17.10)
We see readily that the expected value of the estimator does not depend on q:
E
q
[ˆs
q
] = E
q
[ˆs
p
] = s. (17.11)
The variance of an importance sampling estimator, however, can be greatly sensitive
to the choice of q. The variance is given by
Var[ˆs
q
] = Var[
p(x)f(x)
q(x)
]/n. (17.12)
The minimum variance occurs when q is
q
(x) =
p(x)|f(x)|
Z
, (17.13)
where
Z
is the normalization constant, chosen so that
q
(
x
) sums or integrates to
1 as appropriate. Better importance sampling distributions put more weight where
the integrand is larger. In fact, when
f
(
x
) does not change sign,
Var
[
ˆs
q
] = 0,
meaning that a single sample is sufficient when the optimal distribution is used.
Of course, this is only because the computation of
q
has essentially solved the
original problem, so it is usually not practical to use this approach of drawing a
single sample from the optimal distribution.
Any choice of sampling distribution
q
is valid (in the sense of yielding the
correct expected value), and
q
is the optimal one (in the sense of yielding minimum
variance). Sampling from
q
is usually infeasible, but other choices of
q
can be
feasible while still reducing the variance somewhat.
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CHAPTER 17. MONTE CARLO METHODS
Another approach is to use
biased importance sampling
, which has the
advantage of not requiring normalized
p
or
q
. In the case of discrete variables, the
biased importance sampling estimator is given by
ˆs
BIS
=
n
i=1
p(x
(i)
)
q(x
(i)
)
f(x
(i)
)
n
i=1
p(x
(i)
)
q(x
(i)
)
(17.14)
=
n
i=1
p(x
(i)
)
˜q(x
(i)
)
f(x
(i)
)
n
i=1
p(x
(i)
)
˜q(x
(i)
)
(17.15)
=
n
i=1
˜p(x
(i)
)
˜q(x
(i)
)
f(x
(i)
)
n
i=1
˜p(x
(i)
)
˜q(x
(i)
)
, (17.16)
where
˜p
and
˜q
are the unnormalized forms of
p
and
q
, and the
x
(i)
are the samples
from
q
. This estimator is biased because
E
[
ˆs
BIS
]
=
s
, except asymptotically when
n
and the denominator of equation 17.14 converges to 1. Hence this estimator
is called asymptotically unbiased.
Although a good choice of
q
can greatly improve the efficiency of Monte Carlo
estimation, a poor choice of
q
can make the efficiency much worse. Going back to
equation 17.12, we see that if there are samples of
q
for which
p(x)|f(x)|
q(x)
is large,
then the variance of the estimator can get very large. This may happen when
q
(
x
)
is tiny while neither
p
(
x
) nor
f
(
x
) are small enough to cancel it. The
q
distribution
is usually chosen to be a simple distribution so that it is easy to sample from. When
x
is high dimensional, this simplicity in
q
causes it to match
p
or
p|f|
poorly. When
q
(
x
(i)
)
p
(
x
(i)
)
|f
(
x
(i)
)
|
, importance sampling collects useless samples (summing
tiny numbers or zeros). On the other hand, when
q
(
x
(i)
)
p
(
x
(i)
)
|f
(
x
(i)
)
|
, which
will happen more rarely, the ratio can be huge. Because these latter events are
rare, they may not show up in a typical sample, yielding typical underestimation
of
s
, compensated rarely by gross overestimation. Such very large or very small
numbers are typical when
x
is high dimensional, because in high dimension the
dynamic range of joint probabilities can be very large.
In spite of this danger, importance sampling and its variants have been found
very useful in many machine learning algorithms, including deep learning algo-
rithms. For example, see the use of importance sampling to accelerate training in
neural language models with a large vocabulary (section 12.4.3.3) or other neural
nets with a large number of outputs. See also how importance sampling has been
used to estimate a partition function (the normalization constant of a probability
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CHAPTER 17. MONTE CARLO METHODS
distribution) in section 18.7, and to estimate the log-likelihood in deep directed
models, such as the variational autoencoder, in section 20.10.3. Importance sam-
pling may also be used to improve the estimate of the gradient of the cost function
used to train model parameters with stochastic gradient descent, particularly for
models, such as classifiers, in which most of the total value of the cost function
comes from a small number of misclassified examples. Sampling more difficult
examples more frequently can reduce the variance of the gradient in such cases
(Hinton, 2006).
17.3 Markov Chain Monte Carlo Methods
In many cases, we wish to use a Monte Carlo technique but there is no tractable
method for drawing exact samples from the distribution
p
model
(
x
) or from a good
(low variance) importance sampling distribution
q
(
x
). In the context of deep
learning, this most often happens when
p
model
(
x
) is represented by an undirected
model. In these cases, we introduce a mathematical tool called a
Markov chain
to approximately sample from
p
model
(
x
). The family of algorithms that use Markov
chains to perform Monte Carlo estimates is called
Markov chain Monte Carlo
methods
(MCMC). Markov chain Monte Carlo methods for machine learning are
described at greater length in Koller and Friedman (2009). The most standard,
generic guarantees for MCMC techniques are only applicable when the model
does not assign zero probability to any state. Therefore, it is most convenient
to present these techniques as sampling from an energy-based model (EBM)
p
(
x
)
exp (E(x))
as described in section 16.2.4. In the EBM formulation,
every state is guaranteed to have nonzero probability. MCMC methods are in fact
more broadly applicable and can be used with many probability distributions that
contain zero probability states. However, the theoretical guarantees concerning the
behavior of MCMC methods must be proved on a case-by-case basis for different
families of such distributions. In the context of deep learning, it is most common to
rely on the general theoretical guarantees that naturally apply to all energy-based
models.
To understand why drawing samples from an energy-based model is difficult,
consider an EBM over just two variables, defining a distribution p(a, b). In order
to sample
a
, we must draw
a
from
p
(
a | b
), and in order to sample
b
, we must
draw it from
p
(
b | a
). It seems to be an intractable chicken-and-egg problem.
Directed models avoid this because their graph is directed and acyclic. To perform
ancestral sampling
, one simply samples each of the variables in topological order,
conditioning on each variable’s parents, which are guaranteed to have already been
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CHAPTER 17. MONTE CARLO METHODS
sampled (section 16.3). Ancestral sampling defines an efficient, single-pass method
of obtaining a sample.
In an EBM, we can avoid this chicken-and-egg problem by sampling using a
Markov chain. The core idea of a Markov chain is to have a state
x
that begins
as an arbitrary value. Over time, we randomly update
x
repeatedly. Eventually
x
becomes (very nearly) a fair sample from
p
(
x
). Formally, a Markov chain is
defined by a random state
x
and a transition distribution
T
(
x
| x
) specifying
the probability that a random update will go to state
x
if it starts in state
x
.
Running the Markov chain means repeatedly updating the state
x
to a value
x
sampled from T (x
| x).
To gain some theoretical understanding of how MCMC methods work, it is
useful to reparametrize the problem. First, we restrict our attention to the case
where the random variable
x
has countably many states. We can then represent
the state as just a positive integer
x
. Different integer values of
x
map back to
different states x in the original problem.
Consider what happens when we run infinitely many Markov chains in parallel.
All the states of the different Markov chains are drawn from some distribution
q
(t)
(
x
), where
t
indicates the number of time steps that have elapsed. At the
beginning,
q
(0)
is some distribution that we used to arbitrarily initialize
x
for each
Markov chain. Later,
q
(t)
is influenced by all the Markov chain steps that have
run so far. Our goal is for q
(t)
(x) to converge to p(x).
Because we have reparametrized the problem in terms of positive integer
x
, we
can describe the probability distribution q using a vector v with
q(x = i) = v
i
. (17.17)
Consider what happens when we update a single Markov chain’s state
x
to a
new state x
. The probability of a single state landing in state x
is given by
q
(t+1)
(x
) =
x
q
(t)
(x)T (x
| x). (17.18)
Using our integer parametrization, we can represent the effect of the transition
operator T using a matrix A. We define A so that
A
i,j
= T (x
= i | x = j). (17.19)
Using this definition, we can now rewrite equation 17.18. Rather than writing it in
terms of
q
and
T
to understand how a single state is updated, we may now use
v
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CHAPTER 17. MONTE CARLO METHODS
and
A
to describe how the entire distribution over all the different Markov chains
(running in parallel) shifts as we apply an update:
v
(t)
= Av
(t1)
. (17.20)
Applying the Markov chain update repeatedly corresponds to multiplying by the
matrix
A
repeatedly. In other words, we can think of the process as exponentiating
the matrix A:
v
(t)
= A
t
v
(0)
. (17.21)
The matrix
A
has special structure because each of its columns represents a
probability distribution. Such matrices are called
stochastic matrices
. If there
is a nonzero probability of transitioning from any state
x
to any other state
x
for
some power
t
, then the Perron-Frobenius theorem (Perron, 1907; Frobenius, 1908)
guarantees that the largest eigenvalue is real and equal to 1. Over time, we can
see that all the eigenvalues are exponentiated:
v
(t)
=
V diag(λ)V
1
t
v
(0)
= V diag(λ)
t
V
1
v
(0)
. (17.22)
This process causes all the eigenvalues that are not equal to 1 to decay to zero. Under
some additional mild conditions,
A
is guaranteed to have only one eigenvector
with eigenvalue 1. The process thus converges to a
stationary distribution
,
sometimes also called the equilibrium distribution. At convergence,
v
= Av = v, (17.23)
and this same condition holds for every additional step. This is an eigenvector
equation. To be a stationary point,
v
must be an eigenvector with corresponding
eigenvalue 1. This condition guarantees that once we have reached the stationary
distribution, repeated applications of the transition sampling procedure do not
change the distribution over the states of all the various Markov chains (although
the transition operator does change each individual state, of course).
If we have chosen
T
correctly, then the stationary distribution
q
will be equal
to the distribution
p
we wish to sample from. We describe how to choose
T
in
section 17.4.
Most properties of Markov chains with countable states can be generalized
to continuous variables. In this situation, some authors call the Markov chain a
Harris chain
, but we use the term Markov chain to describe both conditions.
In general, a Markov chain with transition operator
T
will converge, under mild
conditions, to a fixed point described by the equation
q
(x
) = E
xq
T (x
| x), (17.24)
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CHAPTER 17. MONTE CARLO METHODS
which in the discrete case is just rewriting equation 17.23. When
x
is discrete,
the expectation corresponds to a sum, and when
x
is continuous, the expectation
corresponds to an integral.
Regardless of whether the state is continuous or discrete, all Markov chain
methods consist of repeatedly applying stochastic updates until eventually the state
begins to yield samples from the equilibrium distribution. Running the Markov
chain until it reaches its equilibrium distribution is called
burning in
the Markov
chain. After the chain has reached equilibrium, a sequence of infinitely many
samples may be drawn from the equilibrium distribution. They are identically
distributed, but any two successive samples will be highly correlated with each other.
A finite sequence of samples may thus not be very representative of the equilibrium
distribution. One way to mitigate this problem is to return only every
n
successive
samples, so that our estimate of the statistics of the equilibrium distribution is
not as biased by the correlation between an MCMC sample and the next several
samples. Markov chains are thus expensive to use because of the time required to
burn in to the equilibrium distribution and the time required to transition from
one sample to another reasonably decorrelated sample after reaching equilibrium.
If one desires truly independent samples, one can run multiple Markov chains
in parallel. This approach uses extra parallel computation to eliminate latency.
The strategy of using only a single Markov chain to generate all samples and the
strategy of using one Markov chain for each desired sample are two extremes; deep
learning practitioners usually use a number of chains that is similar to the number
of examples in a minibatch and then draw as many samples as are needed from
this fixed set of Markov chains. A commonly used number of Markov chains is 100.
Another difficulty is that we do not know in advance how many steps the
Markov chain must run before reaching its equilibrium distribution. This length
of time is called the
mixing time
. Testing whether a Markov chain has reached
equilibrium is also difficult. We do not have a precise enough theory for guiding us
in answering this question. Theory tells us that the chain will converge, but not
much more. If we analyze the Markov chain from the point of view of a matrix
A
acting on a vector of probabilities
v
, then we know that the chain mixes when
A
t
has effectively lost all the eigenvalues from
A
besides the unique eigenvalue of 1.
This means that the magnitude of the second-largest eigenvalue will determine the
mixing time. In practice, though, we cannot actually represent our Markov chain
in terms of a matrix. The number of states that our probabilistic model can visit
is exponentially large in the number of variables, so it is infeasible to represent
v
,
A
, or the eigenvalues of
A
. Because of these and other obstacles, we usually do
not know whether a Markov chain has mixed. Instead, we simply run the Markov
chain for an amount of time that we roughly estimate to be sufficient, and use
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CHAPTER 17. MONTE CARLO METHODS
heuristic methods to determine whether the chain has mixed. These heuristic
methods include manually inspecting samples or measuring correlations between
successive samples.
17.4 Gibbs Sampling
So far we have described how to draw samples from a distribution
q
(
x
) by repeatedly
updating
x x
T
(
x
| x
). We have not described how to ensure that
q
(
x
) is a
useful distribution. Two basic approaches are considered in this book. The first
one is to derive
T
from a given learned
p
model
, described below with the case of
sampling from EBMs. The second one is to directly parametrize
T
and learn it, so
that its stationary distribution implicitly defines the
p
model
of interest. Examples
of this second approach are discussed in sections 20.12 and 20.13.
In the context of deep learning, we commonly use Markov chains to draw
samples from an energy-based model defining a distribution
p
model
(
x
). In this case,
we want the
q
(
x
) for the Markov chain to be
p
model
(
x
). To obtain the desired
q(x), we must choose an appropriate T (x
| x).
A conceptually simple and effective approach to building a Markov chain
that samples from
p
model
(
x
) is to use
Gibbs sampling
, in which sampling from
T
(
x
| x
) is accomplished by selecting one variable
x
i
and sampling it from
p
model
conditioned on its neighbors in the undirected graph
G
defining the structure of the
energy-based model. We can also sample several variables at the same time as long
as they are conditionally independent given all their neighbors. As shown in the
RBM example in section 16.7.1, all the hidden units of an RBM may be sampled
simultaneously because they are conditionally independent from each other given
all the visible units. Likewise, all the visible units may be sampled simultaneously
because they are conditionally independent from each other given all the hidden
units. Gibbs sampling approaches that update many variables simultaneously in
this way are called block Gibbs sampling.
Alternate approaches to designing Markov chains to sample from
p
model
are
possible. For example, the Metropolis-Hastings algorithm is widely used in other
disciplines. In the context of the deep learning approach to undirected modeling,
it is rare to use any approach other than Gibbs sampling. Improved sampling
techniques are one possible research frontier.
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CHAPTER 17. MONTE CARLO METHODS
17.5 The Challenge of Mixing between Separated
Modes
The primary difficulty involved with MCMC methods is that they have a tendency
to
mix
poorly. Ideally, successive samples from a Markov chain designed to sample
from
p
(
x
) would be completely independent from each other and would visit many
different regions in
x
space proportional to their probability. Instead, especially
in high-dimensional cases, MCMC samples become very correlated. We refer
to such behavior as slow mixing or even failure to mix. MCMC methods with
slow mixing can be seen as inadvertently performing something resembling noisy
gradient descent on the energy function, or equivalently noisy hill climbing on the
probability, with respect to the state of the chain (the random variables being
sampled). The chain tends to take small steps (in the space of the state of the
Markov chain), from a configuration
x
(t1)
to a configuration
x
(t)
, with the energy
E
(
x
(t)
) generally lower or approximately equal to the energy
E
(
x
(t1)
), with a
preference for moves that yield lower energy configurations. When starting from a
rather improbable configuration (higher energy than the typical ones from
p
(
x
)),
the chain tends to gradually reduce the energy of the state and only occasionally
move to another mode. Once the chain has found a region of low energy (for
example, if the variables are pixels in an image, a region of low energy might be a
connected manifold of images of the same object), which we call a mode, the chain
will tend to walk around that mode (following a kind of random walk). Once in a
while it will step out of that mode and generally return to it or (if it finds an escape
route) move toward another mode. The problem is that successful escape routes
are rare for many interesting distributions, so the Markov chain will continue to
sample the same mode longer than it should.
This is very clear when we consider the Gibbs sampling algorithm (section 17.4).
In this context, consider the probability of going from one mode to a nearby mode
within a given number of steps. What will determine that probability is the shape
of the “energy barrier” between these modes. Transitions between two modes
that are separated by a high energy barrier (a region of low probability) are
exponentially less likely (in terms of the height of the energy barrier). This is
illustrated in figure 17.1. The problem arises when there are multiple modes with
high probability that are separated by regions of low probability, especially when
each Gibbs sampling step must update only a small subset of variables whose
values are largely determined by the other variables.
As a simple example, consider an energy-based model over two variables
a
and
b
, which are both binary with a sign, taking on values
1 and 1. If
E
(
a, b
) =
wab
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CHAPTER 17. MONTE CARLO METHODS
for some large positive number
w
, then the model expresses a strong belief that
a
and
b
have the same sign. Consider updating
b
using a Gibbs sampling step with
a
= 1. The conditional distribution over
b
is given by
P
(
b
= 1
| a
= 1) =
σ
(
w
).
If
w
is large, the sigmoid saturates, and the probability of also assigning
b
to be
1 is close to 1. Likewise, if
a
=
1, the probability of assigning
b
to be
1 is
close to 1. According to
P
model
(
a, b
), both signs of both variables are equally likely.
According to
P
model
(
a | b
), both variables should have the same sign. This means
that Gibbs sampling will only very rarely flip the signs of these variables.
In more practical scenarios, the challenge is even greater because we care about
making transitions not only between two modes but more generally between all
the many modes that a real model might contain. If several such transitions are
difficult because of the difficulty of mixing between modes, then it becomes very
expensive to obtain a reliable set of samples covering most of the modes, and
convergence of the chain to its stationary distribution is very slow.
Sometimes this problem can be resolved by finding groups of highly dependent
units and updating all of them simultaneously in a block. Unfortunately, when
the dependencies are complicated, it can be computationally intractable to draw a
sample from the group. After all, the problem that the Markov chain was originally
introduced to solve is this problem of sampling from a large group of variables.
In the context of models with latent variables, which define a joint distribution
p
model
(
x, h
), we often draw samples of
x
by alternating between sampling from
p
model
(
x | h
) and sampling from
p
model
(
h | x
). From the point of view of mixing
rapidly, we would like
p
model
(
h | x
) to have high entropy. From the point of
view of learning a useful representation of
h
, we would like
h
to encode enough
information about
x
to reconstruct it well, which implies that
h
and
x
should have
high mutual information. These two goals are at odds with each other. We often
learn generative models that very precisely encode
x
into
h
but are not able to mix
very well. This situation arises frequently with Boltzmann machines—the sharper
the distribution a Boltzmann machine learns, the harder it is for a Markov chain
sampling from the model distribution to mix well. This problem is illustrated in
figure 17.2.
All this could make MCMC methods less useful when the distribution of interest
has a manifold structure with a separate manifold for each class: the distribution is
concentrated around many modes, and these modes are separated by vast regions
of high energy. This type of distribution is what we expect in many classification
problems, and it would make MCMC methods converge very slowly because of
poor mixing between modes.
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Figure 17.1: Paths followed by Gibbs sampling for three distributions, with the Markov
chain initialized at the mode in both cases. (Left)A multivariate normal distribution
with two independent variables. Gibbs sampling mixes well because the variables are
independent. (Center)A multivariate normal distribution with highly correlated variables.
The correlation between variables makes it difficult for the Markov chain to mix. Because
the update for each variable must be conditioned on the other variable, the correlation
reduces the rate at which the Markov chain can move away from the starting point.
(Right)A mixture of Gaussians with widely separated modes that are not axis aligned.
Gibbs sampling mixes very slowly because it is difficult to change modes while altering
only one variable at a time.
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17.5.1 Tempering to Mix between Modes
When a distribution has sharp peaks of high probability surrounded by regions of
low probability, it is difficult to mix between the different modes of the distribution.
Several techniques for faster mixing are based on constructing alternative versions
of the target distribution in which the peaks are not as high and the surrounding
valleys are not as low. Energy-based models provide a particularly simple way to
do so. So far, we have described an energy-based model as defining a probability
distribution
p(x) exp (E(x)) . (17.25)
Energy-based models may be augmented with an extra parameter
β
controlling
how sharply peaked the distribution is:
p
β
(x) exp (βE(x)) . (17.26)
The
β
parameter is often described as being the reciprocal of the
temperature
,
reflecting the origin of energy-based models in statistical physics. When the
temperature falls to zero, and
β
rises to infinity, the energy-based model becomes
Figure 17.2: An illustration of the slow mixing problem in deep probabilistic models.
Each panel should be read left to right, top to bottom. (Left)Consecutive samples from
Gibbs sampling applied to a deep Boltzmann machine trained on the MNIST dataset.
Consecutive samples are similar to each other. Because the Gibbs sampling is performed
in a deep graphical model, this similarity is based more on semantic than raw visual
features, but it is still difficult for the Gibbs chain to transition from one mode of the
distribution to another, for example, by changing the digit identity. (Right)Consecutive
ancestral samples from a generative adversarial network. Because ancestral sampling
generates each sample independently from the others, there is no mixing problem.
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deterministic. When the temperature rises to infinity, and
β
falls to zero, the
distribution (for discrete x) becomes uniform.
Typically, a model is trained to be evaluated at
β
= 1. However, we can make
use of other temperatures, particularly those where
β <
1.
Tempering
is a general
strategy of mixing between modes of p
1
rapidly by drawing samples with β < 1.
Markov chains based on
tempered transitions
(Neal, 1994) temporarily
sample from higher-temperature distributions to mix to different modes, then
resume sampling from the unit temperature distribution. These techniques have
been applied to models such as RBMs (Salakhutdinov, 2010). Another approach is
to use
parallel tempering
(Iba, 2001), in which the Markov chain simulates many
different states in parallel, at different temperatures. The highest temperature
states mix slowly, while the lowest temperature states, at temperature 1, provide
accurate samples from the model. The transition operator includes stochastically
swapping states between two different temperature levels, so that a sufficiently high-
probability sample from a high-temperature slot can jump into a lower temperature
slot. This approach has also been applied to RBMs (Desjardins et al., 2010; Cho
et al., 2010). Although tempering is a promising approach, at this point it has not
allowed researchers to make a strong advance in solving the challenge of sampling
from complex EBMs. One possible reason is that there are
critical temperatures
around which the temperature transition must be very slow (as the temperature is
gradually reduced) for tempering to be effective.
17.5.2 Depth May Help Mixing
When drawing samples from a latent variable model
p
(
h, x
), we have seen that if
p
(
h | x
) encodes
x
too well, then sampling from
p
(
x | h
) will not change
x
very
much, and mixing will be poor. One way to resolve this problem is to make
h
a
deep representation, encoding
x
into
h
in such a way that a Markov chain in the
space of
h
can mix more easily. Many representation learning algorithms, such as
autoencoders and RBMs, tend to yield a marginal distribution over
h
that is more
uniform and more unimodal than the original data distribution over
x
. It can be
argued that this arises from trying to minimize reconstruction error while using all
the available representation space, because minimizing reconstruction error over
the training examples will be better achieved when different training examples are
easily distinguishable from each other in
h
-space, and thus well separated. Bengio
et al. (2013a) observed that deeper stacks of regularized autoencoders or RBMs
yield marginal distributions in the top-level
h
-space that appeared more spread out
and more uniform, with less of a gap between the regions corresponding to different
modes (categories, in the experiments). Training an RBM in that higher-level
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space allowed Gibbs sampling to mix faster between modes. It remains unclear,
however, how to exploit this observation to help better train and sample from deep
generative models.
Despite the difficulty of mixing, Monte Carlo techniques are useful and are
often the best tool available. Indeed, they are the primary tool used to confront
the intractable partition function of undirected models, discussed next.
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